Probability Course web page: vision.cis.udel.edu/cv March 19, 2003  Lecture 15

Announcements Read Forsyth & Ponce, Chapter 1.2, 1.4, 7.4 on cameras, sampling for Friday

Outline Random variables –Discrete –Continuous Joint, conditional probability Probabilistic inference

Discrete Random Variables A discrete random variable X has a domain of values fx 1, …, x n g that it can assume, each with a particular probability in the range [0, 1] For example, let X = Weather. Then the domain might be fsun, rain, clouds, snowg Use A, B to denote boolean random variables whose domain is ftrue, falseg

Discrete Probability P (X = x i ) is the probability that X has the value x i Can use P (x i ) where random variable is clear –Boolean variables: P (A) ´ P (A = true) and P (:A) ´ P (A = false) The probability distribution P (X) is the vector of probabilities over X ‘s domain: P (X) = (P (X = x 1 ), …, P (X = x n ))

Meaning of Probability Probability can be interpreted as –The strength of our belief or certainty that a random variable has a particular value in the absence of evidence (sometimes called prior probability) –The frequency with which the random variable will have that value if it is repeatedly measured Because a random variable must take one of the values in its domain, probability distributions sum to 1: § i P (X = x i ) = 1

Example: Distribution on Weather So for our example, we might have… –The probabilities for the individual values: P (sun) = 0.7 P (rain) = 0.2 P (clouds) = 0.08 P (snow) = 0.02 –The probability distribution P (Weather) = (0.7, 0.2, 0.08, 0.02) P (Weather = sun)

Joint Probability Joint Probability: The probability that multiple events occur: P (X = x, Y = y) or P (x, y) –E.g.: P (Weather = sun, Temperature = warm ) We can thus define the joint probability distribution P (X, Y) –This is an M 1 £... £ M n table for n random variables with M i values in their domains –Table entries sum to 1

Example: Joint Probability Distribution P (Weather, Temperature ) P (sun, warm ) = 0.5P (sun, cold ) = 0.2 P (rain, warm ) = 0.05P (rain, cold ) = 0.15 P (clouds, warm ) = 0.03 P (clouds, cold ) = 0.05 P (snow, warm ) = P (snow, cold ) = Weather Temperature

Continuous Random Variables When the variable X has a continuous domain of possible values [x low, x high ], it doesn’t make sense to talk about the probability of a particular value. Instead, we define a probability density function (PDF) p(x) such that

Probability Density Function: Properties p(x) is non-negative. By analogy with discrete probability distributions: For n -dimensional joint distributions, the PDF is a surface evaluated over vectors p(x) Most definitions are analogous to discrete versions— just substitute integration for summation

Example: the Normal Distribution Here the PDF is defined by an n -D Gaussian function: Representations of joint PDF for 2-D Gaussians with random variables X, Y Y X

Histograms Definition: Count of instances per bin Example: Random variable brightness is really continuous, but discretized to [0, 255] courtesy of MathWorks Brightness histogram Original image

Histograms as PDF Representations Dividing every bin count by the total count captures frequency of occurrence in that range –E.g., P (brightness 2 [x, x + dx])

Marginalization Summing a discrete joint distribution over all possible values of one random variable effectively removes that variable For a two-variable joint distribution, this means summing all rows or all columns

Example: Marginalization of P (Weather, Temperature ) P (sun, warm ) = 0.5P (sun, cold ) = 0.2 P (rain, warm ) = 0.05P (rain, cold ) = 0.15 P (clouds, warm ) = 0.03 P (clouds, cold ) = 0.05 P (snow, warm ) = P (snow, cold ) = Weather Temperature P (Temperature) P (Weather)

Conditional Probability The conditional probability P (X = x j Y = y) quantifies the change in our beliefs given knowledge of some other event. This “after the evidence” probability is sometimes called the posterior probability on X, and it is defined with the product rule: P (X = x, Y = y) = P (X = x j Y = y) P (Y = y) In terms of joint probability distributions, this is: P (X, Y) = P (X j Y) P (Y) Independence: P (X, Y) = P (X ) P (Y), which implies that P (X j Y) = P (X) remember that these are different distributions

Example: Conditional Probability Distribution P (Temperature j Weather) Divide joint distribution by marginal –E.g., P (warm j sun) = P (sun, warm)/P (sun) Recall P (Temperature) = (0.581, 0.419) P (warm j sun ) = 0.5/0.7 = 0.71 P (cold j sun ) = 0.2/0.7 = 0.29 P (warm j rain ) = 0.05/0.2 = 0.25 P (cold j rain ) = 0.15/0.2 = 0.75 P (warm j clouds ) = 0.03/0.08 = P (cold j clouds ) = 0.05/0.08 = P (warm j snow ) = 0.001/0.02 = 0.05 P (cold j snow ) = 0.019/0.02 = 0.95 Weather Temperature warm cold

Conditioning By the relationship of conditional probability to joint probability P (Y, X) = P (Y j X) P (X) we can write marginalization a different way:

Bayes’ Rule Equating P (X, Y) and P (Y, X) and applying the definition of conditional probability, we have: P (X j Y) P (Y) = P (Y j X) P (X) and so likelihood prior on X posterior on X evidence

Bayes’ Rule By conditioning, the evidence P (Y) is just a normalizing factor that ensures that the posterior sums to 1: Thus, only the likelihood and prior matter:

Bayes’ Rule for Inference Suppose X represents possible hypotheses about some aspect of the world (e.g., the weather today), and Y represents some relevant data we have measured (e.g., thermometer temperature). Inference is the process of reasoning about how likely different values of X are conditioned on Y Bayes’ rule can be a useful inference tool when there is an imbalance in our knowledge or it is difficult to quantify hypothesis probabilities directly Inferring hypothesis values is often called parameter estimation

Maximum a posteriori (MAP): Inference Choose a parameter value x MAP for the hypothesis X that maximizes the probability of the observed data Y = y For discrete distributions, this means calculating the posterior probability P (X j y) for all different values of X For continuous distributions, we may be able to employ differential techniques such as looking for values where the derivative of the posterior is 0

Maximum Likelihood (ML) Inference MAP with a uniform prior (either we don’t know it or believe it to be unimportant):

Example: Estimating the Temperature Suppose we want to know what the weather will be like on the basis of a thermometer reading Say we don’t have direct knowledge of P (Weather j Temperature), but the thermometer reading indicates a chill, and we do know something about P (Temperature j Weather)

Example: Temperature Estimation MAP: From logging past occurrences, we think P (Weather) = ( 0.7, 0.2, 0.08, 0.02). –This leads us to infer that it is sunny, but not by much over rainy ML: Ignoring the weather prior, snow is most likely How much better our estimate is than the other possibilities says something about how good it is—we would be much more certain of sunniness for a warm thermometer reading P (sun j cold) / P (cold j sun ) P (sun ) = 0.29 * 0.7 = P (rain j cold) / P (cold j rain ) P (rain ) = 0.75 * 0.2 = 0.15 P (clouds j cold) / P (cold j clouds ) P (clouds ) = * 0.08 = 0.05 P (snow j cold) / P (cold j snow ) P (snow ) = 0.95 * 0.02 = 0.019