Probability Intro Part II: Bayes Rule
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1 Probability Intro Part II: Bayes Rule Jonathan Pillow Mathematical Tools for Neuroscience (NEU 314) Spring, 2016 lecture 13
2 Quick recap Random variable X takes on different values according to a probability distribution discrete: probability mass function (pmf) continuous: probability density function (pdf) marginalization: summing ( splatting ) conditionalization: slicing
3 conditionalization ( slicing ) 3 2 ( joint divided by marginal )
4 conditionalization ( slicing ) 3 2 ( joint divided by marginal )
5 conditionalization ( slicing ) conditional marginal P(x)
6 conditional densities
7 conditional densities
8 Bayes Rule Conditional Densities likelihood prior Bayes Rule posterior marginal probability of y ( normalizer )
9 A little math: Bayes rule very simple formula for manipulating probabilities P(B A) = conditional probability probability of B given that A occurred P(A B) P(B) P(A) probability of B probability of A simplified form: P(B A) P(A B) P(B)
10 Example: 2 coins A little math: Bayes rule P(B A) P(A B) P(B) one coin is fake: heads on both sides (H / H) one coin is standard: (H / T) You grab one of the coins at random and flip it. It comes up heads. What is the probability that you re holding the fake? p( Fake H) p(h Fake) p(fake) ( 1 ) ( ½ ) = ½ p( Nrml H) p (H Nrml) p(nrml) ( ½ ) ( ½ ) = ¼ probabilities must sum to 1
11 A little math: Bayes rule P(B A) P(A B) P(B) Example: 2 coins start fake normal H H H T p( Fake H) p( Nrml H) p(h Fake) p(fake) ( 1 ) ( ½ ) = ½ p (H Nrml) p(nrml) ( ½ ) ( ½ ) = ¼ probabilities must sum to 1
12 A little math: Bayes rule P(B A) P(A B) P(B) Example: 2 coins start fake normal Experiment #2: It comes up tails. What is the probability that you re holding the fake? H H H T p( Fake T) p(t Fake) p(fake) ( 0 ) ( ½ ) = 0 = 0 p( Nrml T) p (T Nrml) p(nrml) probabilities must sum to 1 ( ½ ) ( ½ ) = ¼ = 1
13 Is the middle circle popping out or in?
14 P( image OUT & light is above) = 1 P(image IN & Light is below) = 1 Image equally likely to be OUT or IN given sensory data alone What we want to know: P(OUT image) vs. P(IN image) Apply Bayes rule: prior P(OUT image) P(image OUT & light above) P(OUT) P(light above) P(IN image) P(image IN & light below ) P(IN) P(light below) Which of these is greater?
15 Bayesian Models for Perception Bayes rule: P(B A) P(A B) P(B) Formula for computing: P(what s in the world sensory data) (This is what our brain wants to know!) B A P(world sense data) P(sense data world) P(world) Posterior (resulting beliefs about the world) Likelihood (given by laws of physics; ambiguous because many world states could give rise to same sense data) Prior (given by past experience)
16 Helmholtz: perception as optimal inference Perception is our best guess as to what is in the world, given our current sensory evidence and our prior experience. helmholtz P(world sense data) P(sense data world) P(world) Posterior (resulting beliefs about the world) Likelihood (given by laws of physics; ambiguous because many world states could give rise to same sense data) Prior (given by past experience)
17 Helmholtz: perception as optimal inference Perception is our best guess as to what is in the world, given our current sensory evidence and our prior experience. helmholtz P(world sense data) P(sense data world) P(world) Posterior (resulting beliefs about the world) Likelihood (given by laws of physics; ambiguous because many world states could give rise to same sense data) Prior (given by past experience)
18 Many different 3D scenes can give rise to the same 2D retinal image The Ames Room
19 Many different 3D scenes can give rise to the same 2D retinal image The Ames Room A B How does our brain go about deciding which interpretation? P(image A) and P(image B) are equal! (both A and B could have generated this image) Let s use Bayes rule: P(A image) = P(image A) P(A) / Z P(B image) = P(image B) P(B) / Z
20 Hollow Face Illusion
21 Hollow Face Illusion Hypothesis #1: face is concave Hypothesis #2: face is convex P(convex video) P(video convex) P(convex) P(concave video) P(video concave) P(concave) posterior likelihood prior P(convex) > P(concave) posterior probability of convex is higher (which determines our percept)
22 prior belief that objects are convex is SO strong we can t over-ride it, even when we know it s wrong! (So your brain knows Bayes rule even if you don t!)
23 Terminology question: When do we call this a likelihood? A: when considered as a function of x (i.e., with y held fixed) note: doesn t integrate to 1. What s it called as a function of y, for fixed x? conditional distribution or sampling distribution
24 independence
25 independence Definition: x, y are independent iff
26 independence Definition: x, y are independent iff In linear algebra terms: 0 1 (outer product)
27 Summary marginalization (splatting) conditionalization (slicing) Bayes rule (prior, likelihood, posterior) independence
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