# Bayes's Theorem and Inverse Probability

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This Demonstration allows you to explore the quantitative relationship between two conditional probability assessments, and , one the inverse of the other, where stands for probability, for a proposition about a "diagnostic signal", and for a proposition about a "state" variable of interest. Typically, the relationship between these inverse probabilities is understood through Bayes's theorem:

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You can vary (1) the red point, , the sensitivity of the diagnostic test; (2) the blue point, , where is the specificity of the diagnostic test; and (3) the purple point, , the base rate of the state. In this notation and are symbols for the negations of propositions and , read as "not " and "not ".

The Demonstration also illustrates Bayes's theorem. Bayes's theorem compares an unconditional probability for the state, , also called a prior probability, with a conditional probability , commonly called a posterior probability. The basic idea here is simple enough: if a signal is more likely given state than it is on average, that is, , then the posterior probability of the state having seen signal should exceed the prior probability for the state, that is, , and vice versa if the signal is less likely given the state. This is logically evident from the expression for Bayes's theorem since the weighted average in the denominator of the right-hand side is in fact . This is clear in the graphic by focusing on the horizontal difference between the blue dot and the vertical line through the purple dot as you vary the underlying parameters specified in the sliders.

Keeping simultaneous track of all of the conditional and unconditional probabilities used in inverse probability reasoning is difficult, especially so when one or more of the probabilities is varying. The red and blue dotted lines in the animation are designed to ease that cognitive load, using the idea of weighted averages as a "constraint" on one's thinking about probabilities of states and signals. For example, when the base rate or prior probability is varied, while and remain constant, must remain a weighted average of the two inverse conditional probabilities and , even though the weights and the inverse conditional probabilities used in determining this average will all be changing as changes! This particular "weighted average" constraint on is illustrated by the blue dotted line. Symmetrically, the red dotted line shows another constraint, all possible weighted averages of the two endpoints and , with varying weights . The key point here is that the two constraints are NOT independent. First, the intersection of the two constraints must occur precisely at the unconditional probabilities . And second, given the red dotted line, that is, given the specification of the sensitivity and specificity of the test via the parameters in the animation, and given the base rate, also specified parametrically in the animation, the blue dotted line is fully determined. The endpoints of the blue dotted line, the two inverse conditional probabilities and , are especially interesting, since it is the difference between them that really indicates how "informative" the diagnostic signal is for the state. It is a worthwhile exercise to use the Demonstration to observe how uninformative a good (i.e., sensitive and specific) diagnostic test can be for very high or very low values of the base rate .

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Contributed by: John Fountain (January 2009)
Open content licensed under CC BY-NC-SA

## Details

It is well known (G. Gigerenzer, Calculated Risks: How to Know When Numbers Deceive You, New York: Simon & Schuster, 2002) that many people, including well-educated professionals, do not reason well about uncertainties, especially when that reasoning involves using conditional probabilities. Conceptually, the logical relationships between "signals", such as the outcome of a diagnostic health test, and "states", such as the presence, absence, or severity of a disease, is often misunderstood. Even when the concepts involved are understood, poor quantitative inferences are frequently made, systematically putting too much emphasis on the result of possibly reliable, but imperfect, diagnostic tests, and too little emphasis on the underlying base rates of the state variable. Moreover, there is often a misplaced "illusion of certainty" in whatever quantitative inferences are eventually made: it takes great computational effort to conduct robustness tests unaided (i.e., with pencil and paper) to see whether small changes in assumed or known uncertainties have much of an impact on the final inference that will be used for decision making.

This Demonstration allows you to explore the quantitative relationship between two conditional probability assessments, and , one the inverse of the other, where stands for probability, for propositions asserting something about a "diagnostic signal", and for propositions about some "state" variable of interest, with the symbol ("not ") indicating the logical negation of the proposition about , explained more completely in the next paragraph. Typically the relationship between these inverse probabilities is understood through Bayes's theorem, which can be represented mathematically as a relationship between four probability assessments, , , , and , whereby specifying the last three determines the first, as shown in the following equation:

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The controls let you vary separately , the sensitivity of the diagnostic test, , with the specificity of the diagnostic test, and , the base rate of the state variable. The visual output design focuses attention on the effect of these changes in a qualitative and quantitative way on the relationship between the inverse probabilities and .

In a typical interpretation of this symbolism stands for probability, and and are indicator variables taking on the values 1 or 0 for some observable binary events. Less abstractly we can interpret and as indicating the truth value of some underlying proposition about observable events (so and are propositions that are the corresponding logical negations of the original propositions). For example might indicate the truth value of a proposition about someone's health, "Jones has osteoporosis", or someone's educational outcomes, "Smith received a grade of B in the course in 2008", so indicates the truth value of the proposition that "Jones does not have osteoporosis" or that "Smith did not receive a grade of B in the course in 2008". Similarly might indicate the truth value of some other underlying proposition about a possibly related observable state of the world. For example might indicate the truth value of the proposition that "Jones has a bone density measurement two standard deviations below the mean for her age as revealed by a DEXA (dual energy x-ray) scan" or that "Smith attended more than half of the tutorials for the course in 2008", with the negation of the relevant proposition.

Logically, there are four possibilities for the values of the indicator variables and considered jointly, , , , . Uncertainty about the variables and may be expressed as a coherent, discrete, joint probability distribution over this four-element set, specifying four elementary probabilities on the space of logically possible joint outcomes : , , , , with obvious notation. Of course, since these probabilities have to add up to 1, there are really only three independent numbers that will completely tie down this joint probability distribution. But specifying three numbers for a joint probability distribution is not the only way of thinking about one's uncertainty about several variables and their possible and probable interrelationships. Nor is it the way used in practice by many of the professional decision makers studied in Gigerenzer's research. This is not surprising when information relevant to calibrating and assessing one's uncertainty is partial and incomplete.

How else might one proceed? For definiteness, take the health example where is the truth value of the proposition "Jones has osteoporosis" and is the truth value of the proposition "Jones has a bone density measurement two standard deviations below the mean for her age as revealed by a DEXA (dual energy x-ray) scan". One might have very good information on the prevalence of the disease, helping to tie down , commonly known as the base rate of the state. Perhaps also there is some limited information on diagnostic test results from those who definitely have osteoporosis, providing information for an assessment of and , where is commonly known as the sensitivity of the diagnostic test: given that Jones has the disease, how likely is it that the test will pick that up and show a positive result? Likewise, other available information sources might help tie down the chances of positive or negative diagnostic test results for those who do not have osteoporosis, and , with commonly known as the specificity of the diagnostic test: given that Jones doesn't have the disease, how likely is it that the test will pick that up and show a negative result? Alternatively, one might have information on the true or false positive rates, or , and true or false negative rates, or , conditional on outcomes of the diagnostic test, or information on the marginal chances of having a positive test result for osteoporosis. With this multiplicity of important but complex ways of thinking about uncertainty about it is no wonder that Gigerenzer finds even well-educated people becoming confused when they try to reason under uncertainty.

The red and blue dotted lines in the animation are helpful in visualizing the constraints involved in making inferences. The red dotted line between the conditional probabilities and traces out the marginal probability , a weighted average of the two endpoint conditionals using the base rate to specify a weighting function. Similarly, the blue dotted line between and traces out the marginal probability , a weighted average of the two endpoint conditionals using to specify a weighting function. A specification of either the three uncertainties or the three uncertainties fully determines the joint probability distribution . The Demonstration and the logic that is usually followed in applying Bayes's theorem specify , so that the other triple of probabilities is fully determined. Anyone curious about what happens if less than three separate pieces of information are used to try to assess uncertainty about signals and states, and the effect those incomplete specifications of uncertainties can have on inverse inference, should have a look at Chapter 3 of F. Lad's Operational Subjective Statistical Methods: A Mathematical, Philosophical, and Historical Introduction, New York: Wiley, 1996. Indeed the geometric reasoning that led to this Demonstration was inspired by the many beautiful, albeit static, 2D and 3D images in Lad's book.

As noted above, Gigerenzer's research shows that well-educated people typically, and easily, become confused about the logical relationships between the many different notions of probability, even when there are only two simple, binary discrete variables involved (signal and state). But confusion does not necessarily lead to a decision-making crisis. It can be an opportunity to learn. Indeed, I have had great success in teaching undergraduate students in game theory about inverse probability using a static version of this Demonstration in conjunction with logical truth tables and Gigerenzer's "natural frequency" language for reasoning with conditional probabilities. Note: these students typically span the spectrum of social and physical sciences and humanities with most having no prior statistical training whatsoever, and yet, by the end of the course, over 80% appear to both understand the ideas involved in Bayes's theorem and inverse probability and are able to accurately solve simple inverse probability problems using these geometric methods. The freely accessible video clips of my Introductory Game Theory courseshow how these ideas are implemented.

Snapshot 1: a diagnostic test that is very good at ruling out negative states and detecting positive states, both sensitivity and specificity at 93%, still gives only a moderate inverse inference (36%) from a positive test result when the original base rate is low (4%)

Snapshot 2: a completely uninformative test

Snapshot 3: a diagnostic test that is good at ruling out negative states but only just better than chance at detecting positive states, yielding a large improvement in predictive probability from a positive test result

## Permanent Citation

John Fountain

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