betting mathematical models in biology

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Here is an outline from SportsLine for understanding basic terms and concepts in the sports wagering industry:. Perhaps the most common question newcomers have when they see sportsbook odds is, "What do the numbers mean? While the plus and minus signs might look confusing at first, they are easily explained. Take the following listing you might see in a sportsbook:. In this example, the Giants are three-point favorites against the Cowboys. The spread is essentially a mathematical formula used to bridge the talent gap between teams and incentivize potential bettors into considering both sides. The spread is a handicap that requires the favored team to win the game by an ascribed number of points in order for the bettor to win his wager on the team.

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Betting mathematical models in biology

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He plans to spend a day or two every week at the CSC, and I asked him if it makes a difference to be physically located with the people he works with. Regular interaction with biologists, Vahid says, is an important part of the atmosphere of the CSC, and key to creative collaborations. My impression is that a lot of its researchers are doing research that is very quantitative — either they use maths in their actual work, or produce very quantitative data for integration into mathematical models.

This is a new area of science. So what specific projects will Vahid be tackling? His primary interest is in thinking about the effect and role of noise and stochasticity, or randomness, in cellular systems. He explained that although every function in a cell is down to some form of biochemical interaction, the timing of those events is random.

But in a single cell, these molecules — the genes themselves — are only present in a few copies. In a traditional biological sense, in each signalling pathway certain genes are thought to behave in certain ways, but in reality every pair of these interactions is quite random.

So cells seem to have evolved ways of designing their networks so that they can filter out this unwanted variability. The way that works is to be clever about how you use these poor components. You can trust it to do its thing even though it relies on movement of electrons, which is basically quite random.

Traditionally people have used mathematical models that pretty much ignore the variability — so-called deterministic models built on differential equations. Initially, Vahid will be working with Sam Marguerat. When you have a change in size, the number of molecules in the cell will change. The large cell has more molecules than the small cell, and the faster the cell grows the faster the number of molecules in a cell change. That brings us back to stochasticity.

They are also helping us anticipate the biological effects of other actions, from pollution to overfishing. In this respect, it is good that models are artificial: they allow us to observe what happens when we change a biological system, without interfering with the real world. Although such models are often simple, they should not be simplistic.

The best modeling studies are those that follow Ross' approach: they are open about their assumptions; clear about the consequences of these assumptions; and where possible test their predictions against real observations. As well as producing results that can be compared with data, models can help us analyze the data itself. The advent of genome sequencing has created a rich source of information for researchers, but unraveling the relationships within the data can be challenging.

Phylogenetic trees are one way of identifying evolutionary patterns in such datasets. By plotting the points at which each species or variant splits into two distinct branches, the trees allow us to visualize the relationship between different parts of a population. However, even for a few variants, there are a large number of possible trees.

By making assumptions about the manner and rate of mutation, we can use models to find the tree that is most likely to capture the observed data. Phylogenetic trees can help us tackle a range of problems, from understanding the evolution of influenza viruses to mapping the diversity of fishes.

When using such techniques, however, it is important to balance complexity with accuracy. Detailed, flexible models will often match the data better than simple, restrictive ones. We must therefore avoid throwing more assumptions into a model than we need to. We can do this by using an 'information criterion', which measures the amount of information that is lost when we use a particular model to describe reality: simplicity and accuracy should be rewarded, and complexity and error penalized.

Models can help us find patterns, but they can also help explain them. After working on codebreaking and computing during the Second World War, Alan Turing turned his attention to developmental biology. In particular, he was interested in what dictates the shape of organisms.

Using a mathematical model, he found that it was possible to reproduce biological patterns with a 'reaction-diffusion system'. This involves two types of chemical processes: local reactions in which substances are transformed into one another, and diffusion, which makes the chemicals spread out over a surface. It was a nice theory, but it wasn't until February that Turing's hypothesis was finally proven experimentally, with researchers showing that a reaction-diffusion system is responsible for the pattern of ridges in the roof of a mouse's mouth.

Without Turing's work, we might have taken far longer to find the cause of these stripes. By proposing such mechanisms, models can therefore support - and even guide - experimental work, suggesting possible explanations for observed results, as well as areas for future investigation. However, such research needs modelers to engage with those running experiments - and the science behind this research - as much as it requires biologists to be aware of the merits of mathematical approaches.

Models have many benefits: they allow us to estimate future outcomes, analyze large amounts of data, and find explanations for observed patterns. Their potential will no doubt continue to increase as computing power does, allowing us to understand complex biological systems from the genetic to population level. The methods will also have applications outside the life sciences: ideas from ecology have recently been used to study networks of financial transactions, for example.

No model is perfect, of course, but they can be valuable tools for comprehending - and questioning - our surroundings. Despite their strengths, however, mathematical methods still meet with hostility. In the recent US election, statisticians Nate Silver and Sam Wang used simple models to predict the results in each state.

By averaging across a large number of polls, weighting each according to their perceived reliability, both came to the conclusion that Obama had a good chance of winning. Much of the media disagreed, preferring to stick with the story that the race was too close to call. Pundits called the models a joke, or accused the statisticians of political bias. In these journalists' view, predicting the election was like predicting a coin toss, or a game of roulette: there was an equal chance the support of the electorate would land on the blue of the Democrats or the red of the Republicans.

Silver and Wang disagreed, and bet on blue. Thanks to their models, they turned out to be right. Correspondence to Adam Kucharski. Reprints and Permissions. Kucharski, A. How to be wrong but useful. Genome Biol 13, Download citation. Published : 26 December Skip to main content. Search all BMC articles Search. Download PDF.

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Indeed, some authors take conditional probability to be the primitive notion, and axiomatize it directly e. There are other formalizations that give up normalization; that give up countable additivity, and even additivity; that allow probabilities to take infinitesimal values positive, but smaller than every positive real number ; that allow probabilities to be imprecise — interval-valued, or more generally represented with sets of precise probability functions; and that treat probabilities comparatively rather than quantitatively.

Given certain probabilities as inputs, the axioms and theorems allow us to compute various further probabilities. However, apart from the assignment of 1 to the universal set and 0 to the empty set, they are silent regarding the initial assignment of probabilities. First, however, let us list some criteria of adequacy for such interpretations. What criteria are appropriate for assessing the cogency of a proposed interpretation of probability? Of course, an interpretation should be precise, unambiguous, non-circular, and use well-understood primitives.

But those are really prescriptions for good philosophizing generally; what do we want from our interpretations of probability , specifically? We begin by following Salmon , 64 , although we will raise some questions about his criteria, and propose some others. He writes:. This criterion requires that there be some method by which, in principle at least, we can ascertain values of probabilities.

It merely expresses the fact that a concept of probability will be useless if it is impossible in principle to find out what the probabilities are…. It might seem that the criterion of admissibility goes without saying. Yet it turns out that the criterion is non-trivial, and indeed if taken seriously would rule out several of the leading interpretations of probability!

As we will see, some of them fail to satisfy countable additivity; for others certain propensity interpretations the status of at least some of the axioms is unclear. Nevertheless, we regard them as genuine candidates. In any case, if we found an inadmissible interpretation that did a wonderful job of meeting the criteria of ascertainability and applicability, then we should surely embrace it.

So let us turn to those criteria. Understanding it in a way acceptable to a strict empiricist or a verificationist may be too restrictive. Most of the work will be done by the applicability criterion. We must say more as Salmon indeed does about what sort of a guide to life probability is supposed to be.

They include:. Non-triviality: an interpretation should make non-extreme probabilities at least a conceptual possibility. Then trivially, all the axioms come out true, so this interpretation is admissible. We would hardly count it as an adequate interpretation of probability , however, and so we need to exclude it. It is essential to probability that, at least in principle, it can take intermediate values.

All of the interpretations that we will present meet this criterion, so we will discuss it no more. Applicability to frequencies: an interpretation should render perspicuous the relationship between probabilities and long-run frequencies. Among other things, it should make clear why, by and large, more probable events occur more frequently than less probable events.

Applicability to rational beliefs: an interpretation should clarify the role that probabilities play in constraining the degrees of belief, or credences , of rational agents. Among other things, knowing that one event is more probable than another, a rational agent will be more confident about the occurrence of the former event. Applicability to rational decisions : an interpretation should make clear how probabilities figure in rational decision-making.

Applicability to science: an interpretation should illuminate paradigmatic uses of probability in science for example, in quantum mechanics and statistical mechanics. Perhaps there are further metaphysical desiderata that we might impose on the interpretations. For example, there appear to be connections between probability and modality. See Skyrms In any case, our list is already long enough to help in our assessment of the leading interpretations on the market.

Some philosophers will insist that not all of these concepts are intelligible; some will insist that one of them is basic, and that the others are reducible to it. Moreover, the boundaries between these concepts are somewhat permeable. And there are intramural disputes within the camps supporting each of these concepts, as we will also see. Be that as it may, it will be useful to keep these concepts in mind.

Sections 3. The classical interpretation owes its name to its early and august pedigree. It was championed by de Moivre and Laplace, and inchoate versions of it may be found in the works of Pascal, Bernoulli, Huygens, and Leibniz.

It assigns probabilities in the absence of any evidence, or in the presence of symmetrically balanced evidence. The guiding idea is that in such circumstances, probability is shared equally among all the possible outcomes, so that the classical probability of an event is simply the fraction of the total number of possibilities in which the event occurs.

It is often presupposed usually tacitly in textbook probability puzzles. We may ask a number of questions about this formulation. When are events of the same kind? What, then, of probabilities in infinite spaces? Different people may be equally undecided about different things, which suggests that Laplace is offering a subjectivist interpretation in which probabilities vary from person to person depending on contingent differences in their evidence.

Thus, it might be claimed, there is no circularity in the classical interpretation after all. However, this move may only postpone the problem, for there is still a threat of circularity, albeit at a lower level. For example, we have a considerable fund of evidence on coin tossing from the results of our own experiments, the testimony of others, our knowledge of some of the relevant physics, and so on.

In the second case, the threat of circularity is more apparent, for it seems that some sort of weighing of the evidence in favor of each outcome is required, and this seems to require a reference to probability. Then it seems that probabilities reside at the base of the interpretation after all. Still, it would be an achievement if all probabilities could be reduced to cases of equal probability. See Zabell for further discussion of the classical interpretation and the principle of indifference.

When the spaces are countably infinite, the spirit of the classical theory may be upheld by appealing to the information-theoretic principle of maximum entropy , a generalization of the principle of indifference championed by Jaynes The more concentrated is the function, the less is its entropy; the more diffuse it is, the greater is its entropy.

For more explanation of this formula see the entry on Information. The principle of maximum entropy enjoins us to select from the family of all probability functions consistent with our background knowledge the function that maximizes this quantity. Things get more complicated in the infinite case, since there cannot be a flat assignment over denumerably many outcomes, on pain of violating the standard probability calculus with countable additivity.

Rather, the best we can have are sequences of progressively flatter assignments, none of which is truly flat. Let us turn now to uncountably infinite spaces. It is easy — all too easy — to assign equal probabilities to the points in such a space: each gets probability 0. Non-trivial probabilities arise when uncountably many of the points are clumped together in larger sets.

They all arise in uncountable spaces and turn on alternative parametrizations of a given problem that are non-linearly related to each other. Some presentations are needlessly arcane; length and area suffice to make the point.

The following example adapted from van Fraassen nicely illustrates how Bertrand-style paradoxes work. This is already disastrous, as we cannot allow the same event to have two different probabilities especially if this interpretation is to be admissible! And so on for all of the infinitely many equivalent reformulations of the problem in terms of the fourth, fifth, … power of the length, and indeed in terms of every non-zero real-valued exponent of the length.

What, then, is the probability of the event in question? The paradox arises because the principle of indifference can be used in incompatible ways. And so it goes, for all the other reformulations of the problem. We cannot meet any pair of these constraints simultaneously, let alone all of them.

Jaynes attempts to save the principle of indifference and to extend the principle of maximum entropy to the continuous case, with his invariance condition : in two problems where we have the same knowledge, we should assign the same probabilities. He regards this as a consistency requirement. For any problem, we have a group of admissible transformations, those that change the problem into an equivalent form.

Various details are left unspecified in the problem; equivalent formulations of it fill in the details in different ways. Any probability assignment that meets this condition is called an invariant assignment. Ideally, our problem will have a unique invariant assignment. To be sure, things will not always be ideal; but sometimes they are, in which case this is surely progress on Bertrand-style problems.

And in any case, for many garden-variety problems such technical machinery will not be needed. Suppose I tell you that a prize is behind one of three doors, and you get to choose a door. It seems implausible that we should worry about some reparametrization of the problem that would yield a different answer. To be sure, Bertrand-style problems caution us that there are limits to the principle of indifference.

But arguably we must just be careful not to overstate its applicability. How does the classical theory of probability fare with respect to our criteria of adequacy? Let us begin with admissibility. Laplacean classical probabilities obey non-negativity and normalization, but they are only finitely additive de Finetti So they do not obey the full Kolmogorov probability calculus, but they provide an interpretation of the elementary theory.

Classical probabilities are ascertainable, assuming that the space of possibilities can be determined in principle. They bear a relationship to the credences of rational agents; the circularity concern, as we saw above, is that the relationship is vacuous, and that rather than constraining the credences of a rational agent in an epistemically neutral position, they merely record them.

Without supplementation, the classical theory makes no contact with frequency information. However the coin happens to land in a sequence of trials, the possible outcomes remain the same. Indeed, even if we have strong empirical evidence that the coin is biased towards heads with probability, say, 0. Thus, inductive learning is possible — though not by classical probabilities per se , but rather thanks to this further rule. And we must ask whether such learning can be captured once and for all by such a simple formula, the same for all domains and events.

We will return to this question when we discuss the logical interpretation below. Science apparently invokes at various points probabilities that look classical. Bose-Einstein statistics, Fermi-Dirac statistics, and Maxwell-Boltzmann statistics each arise by considering the ways in which particles can be assigned to states, and then applying the principle of indifference to different subdivisions of the set of alternatives, Bertrand-style.

The trouble is that Bose-Einstein statistics apply to some particles e. None of this can be determined a priori , as the classical interpretation would have it. Moreover, the classical theory purports to yield probability assignments in the face of ignorance. But as Fine writes:. This brings us to one of the chief points of controversy regarding the classical interpretation.

Critics accuse the principle of indifference of extracting information from ignorance. Proponents reply that it rather codifies the way in which such ignorance should be epistemically managed — for anything other than an equal assignment of probabilities would represent the possession of some knowledge. Critics counter-reply that in a state of complete ignorance, it is better to assign imprecise probabilities perhaps ranging over the entire [0, 1] interval , or to eschew the assignment of probabilities altogether.

However, they generalize it in two important ways: the possibilities may be assigned unequal weights, and probabilities can be computed whatever the evidence may be, symmetrically balanced or not. In any case, it is significant that the logical interpretation provides a framework for induction. However, by far the most systematic study of logical probability was by Carnap.

His formulation of logical probability begins with the construction of a formal language. In he considers a class of very simple languages consisting of a finite number of logically independent monadic predicates naming properties applied to countably many individual constants naming individuals or variables, and the usual logical connectives.

The strongest consistent statements that can be made in a given language describe all of the individuals in as much detail as the expressive power of the language allows. They are conjunctions of complete descriptions of each individual, each description itself a conjunction containing exactly one occurrence negated or unnegated of each predicate of the language. Call these strongest statements state descriptions. Call a structure description a maximal set of state descriptions, each of which can be obtained from another by some permutation of the individual names.

For this language, the state descriptions are:. This will manifest itself in the inductive support that hypotheses can gain from appropriate evidence statements. Note, however, that infinitely many confirmation functions, defined by suitable choices of the initial measure, allow learning from experience. Define a family of predicates to be a set of predicates such that, for each individual, exactly one member of the set applies, and consider first-order languages containing a finite number of families.

Carnap focuses on the special case of a language containing only one-place predicates. See Maher for rebuttals of some of these objections and for defenses of the program. Also, it turns out that for any such setting, a universal statement in an infinite universe always receives zero confirmation, no matter what the finite evidence. Many find this counterintuitive, since laws of nature with infinitely many instances can apparently be confirmed.

Earman discusses the prospects for avoiding the unwelcome result. Goodman taught us: that the future will resemble the past in some respect is trivial; that it will resemble the past in all respects is contradictory. And we may continue: that a probability assignment can be made to respect some symmetry is trivial; that one can be made to respect all symmetries is contradictory.

This threatens the whole program of logical probability. Logical probabilities are admissible. It is easily shown that they satisfy finite additivity, and given that they are defined on finite sets of sentences, the extension to countable additivity is trivial. Given a choice of language, the values of a given confirmation function are ascertainable; thus, if this language is rich enough for a given application, the relevant probabilities are ascertainable.

The problem of arbitrariness of the confirmation function also hampers the extent to which the logical interpretation can truly illuminate the connection between probabilities and frequencies. The arbitrariness problem, moreover, stymies any compelling connection between logical probabilities and rational credences. Thus, the growth of science may overthrow any particular confirmation theory.

There is something of the snake eating its own tail here, since logical probability was supposed to explicate the confirmation of scientific theories. We have seen that the later Carnap relaxed his earlier aspiration to find a unique confirmation function, allowing a continuum of such functions displaying a wide range of inductive cautiousness. Various critics of logical probabilities believe that he did not go far enough — that even his later systems constrain inductive learning beyond what is rationally required.

This recalls the classic debate earlier in the 20 th century between Keynes, a famous proponent of logical probabilities, and Ramsey, an equally famous opponent. Ramsey ; was skeptical of there being any non-trivial relations of logical probability: he said that he could not discern them himself, and that others disagree about them. This skepticism led him to formulate his enormously influential version of the subjective interpretation of probability, to be discussed shortly.

One might insist, however, that there are non-trivial probabilistic evidential relations, even if they are not logical. It may not be a matter of logic that the sun will probably rise tomorrow, given our evidence, yet there still seems to be an objective sense in which it probably will, given our evidence. In a crime investigation, there may be a fact of the matter of how strongly the available evidence supports the guilt of various suspects.

This does not seem to be a matter of logic—nor of physics, nor of what anyone happens to think, nor of how the facts in the actual world turn out. It seems to be a matter, rather, of evidential probabilities. However, one might adopt other conceptions of evidence, and one might even take evidential probabilities to link any two propositions whatsoever.

Williamson maintains that evidential probabilities are not logical—in particular, they are not syntactically definable. Are evidential probabilities admissible? So admissibility is built into the very specification of P. Are they ascertainable? However, some authors are skeptical that there are such things as evidential probabilities—e. Joyce He also argues that there is more than one sense in which evidence tells for or against a hypothesis.

Moreover, one may resist demands for an operational definition of evidential probabilities, while seeking some further understanding of them in terms of other theoretical concepts. Williamson argues against this proposal; Eder forthcoming defends it, and she offers several ways of interpreting evidential probabilities in terms of ideal subjective probabilities.

If some such way is tenable, evidential probabilities would presumably enjoy whatever applicability that such subjective probabilities have. This brings us to our next interpretation of probability. According to the subjective or personalist or Bayesian interpretation, probabilities are degrees of confidence, or credences, or partial beliefs of suitable agents.

Thus, we really have many interpretations of probability here— as many as there are suitable agents. What makes an agent suitable? What we might call unconstrained subjectivism places no constraints on the agents — anyone goes, and hence anything goes. Various studies by psychologists are taken to show that people commonly violate the usual probability calculus in spectacular ways. See, e. We clearly do not have here an admissible interpretation with respect to any probability calculus , since there is no limit to what degrees of confidence agents might have.

More promising, however, is the thought that the suitable agents must be, in a strong sense, rational. A rational agent is required to be logically consistent, now taken in a broad sense. These subjectivists argue that this implies that the agent obeys the axioms of probability although perhaps with only finite additivity , and that subjectivism is thus to this extent admissible. Before we can present this argument, we must say more about what degrees of belief are.

Subjective probabilities have long been analyzed in terms of betting behavior. Here is a classic statement by de Finetti :. This presupposition may fail. For now, however, let us waive these concerns, and turn to an important argument that uses the betting analysis purportedly to show that rational degrees of belief must conform to the probability calculus with at least finite additivity. A Dutch book is a series of bets bought and sold at prices that collectively guarantee loss, however the world turns out.

Suppose we identify your credences with your betting prices. Ramsey notes, and it can be easily proven e. Equally important, and often neglected, is the converse theorem that establishes how you can avoid such a predicament. If your subjective probabilities conform to the probability calculus, then no Dutch book can be made against you Kemeny ; your probability assignments are then said to be coherent. Williamson extends the Dutch Book argument to countable additivity: if your credences violate countable additivity, then you are susceptible to a Dutch book with infinitely many bets.

Conformity to the full probability calculus thus seems to be necessary and sufficient for coherence. Note, however, that de Finetti—the arch subjectivist and proponent of the Dutch Book argument—was an opponent of countable additivity e.

But let us return to the betting analysis of credences. The betting analysis gives an operational definition of subjective probability, and indeed it inherits some of the difficulties of operationalism in general, and of behaviorism in particular. Moreover, as Ramsey points out, placing the very bet may alter your state of opinion.

Trivially, it does so regarding matters involving the bet itself e. Less trivially, placing the bet may change the world, and hence your opinions, in other ways. And then the bet may concern an event such that, were it to occur, you would no longer value the pay-off the same way. During the August 11, solar eclipse in the UK, a man placed a bet that would have paid a million pounds if the world came to an end. The problems may be avoided by identifying your degree of belief in a proposition with the betting price you regard as fair, whether or not you enter into such a bet; it corresponds to the betting odds that you believe confer no advantage or disadvantage to either side of the bet Howson and Urbach At your fair price, you should be indifferent between taking either side.

For example, a sum that can be divided into only parts will leave probability measurements imprecise beyond the second decimal place, conflating probabilities that should be distinguished e. More significantly, if utility is not a linear function of such sums, then the size of the prize will make a difference to the putative probability: winning a dollar means more to a pauper more than it does to Bill Gates, and this may be reflected in their betting behaviors in ways that have nothing to do with their genuine probability assignments.

De Finetti responds to this problem by suggesting that the prizes be kept small; that, however, only creates the opposite problem that agents may be reluctant to bother about trifles, as Ramsey points out. Better, then, to let the prizes be measured in utilities: after all, utility is infinitely divisible, and utility is a linear function of utility.

After all, there is a sense in which every decision is a bet, as Ramsey observed. Utilities desirabilities of outcomes, their probabilities, and rational preferences are all intimately linked. And most remarkably, Ramsey and later, Savage and Jeffrey derives both probabilities and utilities from rational preferences alone. The result of a coin toss is typically like this for most of us.

He is then able to define equality of differences in utility for any outcomes over which the agent has preferences. It turns out that ratios of utility-differences are invariant — the same whichever representative utility function we choose. This fact allows Ramsey to define degrees of belief as ratios of such differences.

Ramsey shows that degrees of belief so derived obey the probability calculus with finite additivity. For a given set of such preferences, he generates a class of utility functions, each a positive linear transformation of the other i. See Buchak for more discussion.

Some of the difficulties with the behavioristic betting analysis of degrees of belief can now be resolved by moving to an analysis of degrees of belief that is functionalist in spirit. There is a deep issue that underlies all of these accounts of subjective probability. They all presuppose the existence of necessary connections between desire-like states and belief-like states, rendered explicit in the connections between preferences and probabilities.

In response, one might insist that such connections are at best contingent, and indeed can be imagined to be absent. Think of an idealized Zen Buddhist monk, devoid of any preferences, who dispassionately surveys the world before him, forming beliefs but no desires. It could be replied that such an agent is not so easily imagined after all — even if the monk does not value worldly goods, he will still prefer some things to others e.

Once desires enter the picture, they may also have unwanted consequences. The derivation of them from preferences makes them ascertainable to the extent that his or her preferences are known. The expected utility representation makes it virtually analytic that an agent should be guided by probabilities — after all, the probabilities are her own, and they are fed into the formula for expected utility in order to determine what it is rational for her to do. So the applicability to rational decision criterion is clearly met.

But do they function as a good guide? Here it is useful to distinguish different versions of subjectivism. Orthodox Bayesians in the style of de Finetti recognize no rational constraints on subjective probabilities beyond:. This is a permissive epistemology, licensing doxastic states that we would normally call crazy. Thus, you could assign probability 1 to this sentence ruling the universe, while upholding such extreme subjectivism.

Some subjectivists impose the further rationality requirement of regularity : anything that is possible in an appropriate sense gets assigned positive probability. It is meant to capture a form of open-mindedness and responsiveness to evidence. But then, perhaps unintuitively, someone who assigns probability 0. Probabilistic coherence plays much the same role for degrees of belief that consistency plays for ordinary, all-or-nothing beliefs. It seems, then, that the subjectivist needs something more.

And various subjectivists offer more. This resonates with more recent proposals e. Since relative frequencies obey the axioms of probability up to finite additivity , it is thought that rational credences, which strive to track them, should do so also.

However, rational credences may strive to track various things. For example, we are often guided by the opinions of experts. We consult our doctors on medical matters, our weather forecasters on meteorological matters, and so on. This idea may be codified as follows:. For example, if you regard the local weather forecaster as an expert on your local weather, and she assigns probability 0. More generally, we might speak of an entire probability function as being such a guide for an agent over a specified set of propositions.

We may go still further. There may be universal expert functions for large classes of rational agents, and perhaps all of them. The Principle of Direct Probability regards the relative frequency function as a universal expert function for all rational agents; we have already seen the importance that proponents of calibration place on it.

Hacking :. Lewis posits a similar expert role for the objective chance function, ch , for all rational initial credences in his Principal Principle here simplified [ 8 ] :. For example, a rational agent who somehow knows that a particular coin toss lands heads is surely not required to assign. The other expert principles surely need to be suitably qualified — otherwise they face analogous counterexamples. Yet strangely, the Principal Principle is the only expert principle about which concerns about inadmissible evidence have been raised in the literature.

The ultimate expert, presumably, is the truth function — the function that assigns 1 to all the true propositions and 0 to all the false ones. So all of the proposed expert probabilities above should really be regarded as defeasible. Joyce portrays the rational agent as estimating truth values, seeking to minimize a measure of distance between them and her probability assignments—that is, to maximize the accuracy of those assignments.

In short, non-probabilistic credences are accuracy-dominated by probabilistic credences. There are some unifying themes in these putative constraints on subjective probability. We have been gradually adding more and more constraints on rational credences, putatively demanded by rationality.

Recall that Carnap first assumed that there was a unique confirmation function, and then relaxed this assumption to allow a plurality of such functions. We now seem to be heading in the opposite direction: starting with the extremely permissive orthodox Bayesianism, we are steadily reducing the class of rationally permissible credence functions. So far the constraints that we have admitted have not been especially evidence -driven.

The lines of demarcation are not sharp, and subjective Bayesianism may be regarded as a somewhat indeterminate region on a spectrum of views that morph into objective Bayesianism. At one end lies an extreme form of subjective Bayesianism, according to which rational credences are constrained only by the probability calculus and updating by conditionalization.

But both objective Bayesians and subjective Bayesians may adopt less extreme positions, and typically do. For example, Jon Williamson is an objective Bayesian, but not an extreme one. He adds to the probability calculus the constraints of being calibrated with evidence, and otherwise equivocating between basic outcomes, especially appealing to versions of maximum entropy. As such, his view is a descendant of the classical interpretation and its generalization due to Jaynes. Gamblers, actuaries and scientists have long understood that relative frequencies bear an intimate relationship to probabilities.

Frequency interpretations posit the most intimate relationship of all: identity. A simple version of frequentism, which we will call finite frequentism , attaches probabilities to events or attributes in a finite reference class in such a straightforward manner:. The crucial difference, however, is that where the classical interpretation counted all the possible outcomes of a given experiment, finite frequentism counts actual outcomes.

It is thus congenial to those with empiricist scruples. Finite frequentism gives an operational definition of probability, and its problems begin there. More than that, it seems to be built into the very notion of probability that such misleading results can arise. Indeed, in many cases, misleading results are guaranteed.

Starting with a degenerate case: according to the finite frequentist, a coin that is never tossed, and that thus yields no actual outcomes whatsoever, lacks a probability for heads altogether; yet a coin that is never measured does not thereby lack a diameter. Perhaps even more troubling, a coin that is tossed exactly once yields a relative frequency of heads of either 0 or 1, whatever its bias.

Or we can imagine a unique radiocative atom whose probabilities of decaying at various times obey a continuous law e. Nonetheless, it seems natural to think of non-extreme probabilities attaching to some, and perhaps all, of them. Among other things, this rules out irrational-valued probabilities; yet our best physical theories say otherwise. Furthermore, there is a sense in which any of these problems can be transformed into the problem of the single case. Suppose that we toss a coin a thousand times.

We can regard this as a single trial of a thousand-tosses-of-the-coin experiment. Yet we do not want to be committed to saying that that experiment yields its actual result with probability 1. The problem of the single case is that the finite frequentist fails to see intermediate probabilities in various places where others do. There is also the converse problem: the frequentist sees intermediate probabilities in various places where others do not.

Our world has myriad different entities, with myriad different attributes. We can group them into still more sets of objects, and then ask with which relative frequencies various attributes occur in these sets. Many such relative frequencies will be intermediate; the finite frequentist automatically identifies them with intermediate probabilities. But it would seem that whether or not they are genuine probabilities , as opposed to mere tallies, depends on the case at hand. Bare ratios of attributes among sets of disparate objects may lack the sort of modal force that one might expect from probabilities.

I belong to the reference class consisting of myself, the Eiffel Tower, the southernmost sandcastle on Santa Monica Beach, and Mt Everest. Some frequentists notably Venn , Reichenbach , and von Mises among others , partly in response to some of the problems above, have gone on to consider infinite reference classes, identifying probabilities with limiting relative frequencies of events or attributes therein.

Thus, we require an infinite sequence of trials in order to define such probabilities. But what if the actual world does not provide an infinite sequence of trials of a given experiment? Indeed, that appears to be the norm, and perhaps even the rule. In that case, we are to identify probability with a hypothetical or counterfactual limiting relative frequency.

We are to imagine hypothetical infinite extensions of an actual sequence of trials; probabilities are then what the limiting relative frequencies would be if the sequence were so extended. We might thus call this interpretation hypothetical frequentism :. Note that at this point we have left empiricism behind. A modal element has been injected into frequentism with this invocation of a counterfactual; moreover, the counterfactual may involve a radical departure from the way things actually are, one that may even require the breaking of laws of nature.

Think what it would take for the coin in my pocket, which has only been tossed once, to be tossed infinitely many times — never wearing out, and never running short of people willing to toss it! One may wonder, moreover, whether there is always — or ever — a fact of the matter of what such counterfactual relative frequencies are.

Limiting relative frequencies, we have seen, must be relativized to a sequence of trials. Herein lies another difficulty. By suitably reordering these results, we can make the sequence converge to any value in [0, 1] that we like.

But there may be more than one natural ordering. Imagine the tosses taking place on a train that shunts backwards and forwards on tracks that are oriented west-east. Then the spatial ordering of the results from west to east could look very different. Why should one ordering be privileged over others? A well-known objection to any version of frequentism is that relative frequencies must be relativised to a reference class.

Consider a probability concerning myself that I care about — say, my probability of living to age I belong to the class of males, the class of non-smokers, the class of philosophy professors who have two vowels in their surname, … Presumably the relative frequency of those who live to age 80 varies across most of these reference classes.

What, then, is my probability of living to age 80? It seems that there is no single frequentist answer. Instead, there is my probability-qua-male, my probability-qua-non-smoker, my probability-qua-male-non-smoker, and so on. This is an example of the so-called reference class problem for frequentism although it can be argued that analogues of the problem arise for the other interpretations as well [ 10 ].

And as we have seen in the previous paragraph, the problem is only compounded for limiting relative frequencies: probabilities must be relativized not merely to a reference class, but to a sequence within the reference class. We might call this the reference sequence problem. The beginnings of a solution to this problem would be to restrict our attention to sequences of a certain kind, those with certain desirable properties.

For example, there are sequences for which the limiting relative frequency of a given attribute does not exist; Reichenbach thus excludes such sequences. Von Mises gives us a more thoroughgoing restriction to what he calls collectives — hypothetical infinite sequences of attributes possible outcomes of specified experiments that meet certain requirements.

Von Mises imposes these axioms:. Note that a constant sequence such as H, H, H, …, in which the limiting relative frequency is the same in any infinite subsequence, trivially satisfies the axiom of randomness. This puts some strain on the terminology — offhand, such sequences appear to be as non -random as they come — although to be sure it is desirable that probabilities be assigned even in such sequences.

Collectives are abstract mathematical objects that are not empirically instantiated, but that are nonetheless posited by von Mises to explain the stabilities of relative frequencies in the behavior of actual sequences of outcomes of a repeatable random experiment. Church renders precise the notion of a place selection as a recursive function. Von Mises embraces this consequence, insisting that the notion of probability only makes sense relative to a collective. Some critics believe that rather than solving the problem of the single case, this merely ignores it.

He introduced the notion of a collective because he believed that the regularities in the behavior of certain actual sequences of outcomes are best explained by the hypothesis that those sequences are initial segments of collectives. But this is curious: we know for any actual sequence of outcomes that they are not initial segments of collectives, since we know that they are not initial segments of infinite sequences.

Let us see how the frequentist interpretations fare according to our criteria of adequacy. Finite relative frequencies of course satisfy finite additivity. In a finite reference class, only finitely many events can occur, so only finitely many events can have positive relative frequency. In that case, countable additivity is satisfied somewhat trivially: all but finitely many terms in the infinite sum will be 0.

Finite frequentism has no trouble meeting the ascertainability criterion, as finite relative frequencies are in principle easily determined. The same cannot be said of limiting relative frequencies. It might seem that the frequentist interpretations resoundingly meet the applicability to frequencies criterion. Finite frequentism meets it all too well, while hypothetical frequentism meets it in the wrong way. If anything, finite frequentism makes the connection between probabilities and frequencies too tight, as we have already observed.

A fair coin that is tossed a million times is very unlikely to land heads exactly half the time; one that is tossed a million and one times is even less likely to do so! Facts about finite relative frequencies should serve as evidence, but not conclusive evidence, for the relevant probability assignments. Hypothetical frequentism fails to connect probabilities with finite frequencies. It connects them with limiting relative frequencies, of course, but again too tightly: for even in infinite sequences, the two can come apart.

A fair coin could land heads forever, even if it is highly unlikely to do so. To be sure, science has much interest in finite frequencies, and indeed working with them is much of the business of statistics. Whether it has any interest in highly idealized, hypothetical extensions of actual sequences, and relative frequencies therein, is another matter.

The applicability to rational beliefs and to rational decisions go much the same way. Such beliefs and decisions are guided by finite frequency information, but they are not guided by information about limits of hypothetical frequencies, since one never has such information. Like the frequency interpretations, propensity interpretations regard probabilities as objective properties of entities in the real world. Walpole, Raymond Myers, Sharon L. Myers and Keying E. It is really thorough, takes one definition at a time, and builds on top of that.

The structuring and writing is top class, and the examples are well chosen. Don't worry if you are not an engineer. When using examples they have taken them from the domain of engineering, eg "A factory produces so and so many items per hour, and only so and so many can be broken, But they don't involve engineering science such as statics, aerodynamics, electronics, thermodynamics or any such things. This means that everyone can understand the book, it does not even help to have an engineering background.

An Introduction to Probability and Random Processes by Kenneth Baclawski and Gian-Carlo Rota is very good, though it does require the reader to have or develop mathematical maturity. While pretty elementary, it provides proofs of all the main results in probability theory, something you would not find in most other elementary textbooks.

It also has plenty of solved exercises and examples. By far the best is. The probability course on coursera by prof santosh venkatesh. You will be surprised to see your understanding grow along as you move along throw these fascinating vedios. If there would have been an Oscar for prob courses. Sign up to join this community. The best answers are voted up and rise to the top.

What is the best book to learn probability? Ask Question. Asked 9 years, 10 months ago. Active 1 year, 7 months ago. Viewed k times. Improve this question. Eduardo Xavier. The books I have always gaps on explanations and that's make me crazy Answerers should explain which of these they are talking about.

It is very inefficient and a waste of people's time to ask for a spray of all possible answers. In fact I think this is as yet not a real question and I am voting to close Clark Apr 11 '11 at I believe you're in a bad day. Cheers, mate!! Show 5 more comments. Active Oldest Votes. Improve this answer. It also doesn't cover in any depth several applications that are generally treated as standard, such as Markov chains, random walks, characteristic functions, etc.

It certainly doesn't cover enough to say, prepare for a course on stochastic differential equations. Is there something to read for the "other side" of these lines? Add a comment. Out of the two Ross books which one would you recommend for better understanding and problem-solving skills? I know this is from long ago but if someone could answer it would be helpful.

There are some questions in there that are quite difficult, so I think this book is more targeted toward an advanced undergraduate. I haven't looked at the Probability Models book, though I would presume it has a lot of overlap with First Course.

As he settled in, he spoke to Susan Watts about his love of maths, physics and biology, and what each can learn from the other.

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Betting mathematical models in biology Follow Us Twitter Facebook Instagram. Metrics details. Understanding more about random behaviour in cells may one day lead to better treatments. Rights and permissions Reprints and Permissions. Search all BMC articles Search.
Betting mathematical models in biology Silver and Wang disagreed, and bet on blue. Thanks to their models, they betting advice application out to be right. Traditionally people have used mathematical models that pretty much ignore the variability — so-called deterministic models built on differential equations. This is a new area of science. In these journalists' view, predicting the election was like predicting a coin toss, or a game of roulette: there was an equal chance the support of the electorate would land on the blue of the Democrats or the red of the Republicans. Phylogenetic trees are one way of identifying evolutionary patterns in such datasets.
Fixed limit betting rules in blackjack When you have a change in size, the number of molecules in the cell will poker betting. Their analysis was followed by a sort of Hippocratic Oath, which began: 'I will remember that I didn't make the world, and it doesn't satisfy my equations'. Their potential will no doubt continue to increase as computing power does, allowing us to understand complex biological systems from the genetic to population level. Seeing the problems created by their field, two prominent financial mathematicians published a 'Financial Modelers' Manifesto'. Although people might make small profits in the short term, eventually solvency would get in the way of strategy.


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An unusual presentation of how mathematical models are designed, built, and validated, Calculated Bets also includes a list of modeling projects with online. The martingale involved betting on black or red. Ross therefore outlined a mathematical model to demonstrate what might happen if. What is the current place of mathematics in problem gambling research, of the decision to gamble include not only the gambler's biological.