CSE 473/573 Computer Vision and Image Processing (CVIP) Ifeoma Nwogu Lecture 27 – Overview of probability concepts 1 Schedule • Last class – Object recognition and bag-of-words • Today – Overview of probability models • Readings for today: – Lecture notes 2 Random variables • A random variable x denotes a quantity that is uncertain • May be result of experiment (flipping a coin) or a real world measurements (measuring temperature) • If observe several instances of x we get different values • Some values occur more than others and this information is captured by a probability distribution Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 3 Discrete Random Variables Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 4 Continuous Random Variable Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 5 Joint Probability • Consider two random variables x and y • If we observe multiple paired instances, then some combinations of outcomes are more likely than others • This is captured in the joint probability distribution • Written as Pr(x,y) • Can read Pr(x,y) as “probability of x and – where x and y are scalars Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 6 Joint Probability (2) • We can definitely have more than two random variables and we would write Pr(x,y,z) • We may also write Pr(x) - the joint probability of all of the elements of the multidimensional variable x = [x1,x2….xn] and if y = [y1,y2….yn] then we can write Pr(x,y) to represent the joint distribution of all of the elements from multidimensional variables x and y. Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 7 Joint Probability (3) 2D Hinton diagram Joint distributions of one discrete and one continuous variable Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 8 Marginalization We can recover probability distribution of any variable in a joint distribution by integrating (or summing) over the other variables Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 9 Marginalization We can recover probability distribution of any variable in a joint distribution by integrating (or summing) over the other variables Scale and Normalization Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 10 Marginalization We can recover probability distribution of any variable in a joint distribution by integrating (or summing) over the other variables Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 11 Marginalization We can recover probability distribution of any variable in a joint distribution by integrating (or summing) over the other variables Works in higher dimensions as well – leaves joint distribution between whatever variables are left Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 12 Conditional Probability • Conditional probability of x given that y=y1 is relative propensity of variable x to take different outcomes given that y is fixed to be equal to y1. • Written as Pr(x | y=y1) Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 13 Conditional Probability • Conditional probability can be extracted from joint probability • Extract appropriate slice and normalize Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 14 Conditional Probability • More usually written in compact form • Can be re-arranged to give Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 15 Conditional Probability • This idea can be extended to more than two variables Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 16 Bayes’ Rule From before: Combining: Re-arranging: Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 17 Bayes’ Rule Terminology Likelihood – propensity for observing a certain value of x given a certain value of y Posterior – what we know about y after seeing x Prior – what we know about y before seeing x Evidence –a constant to ensure that the left hand side is a valid distribution Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 18 Independence • If two variables x and y are independent then variable x tells us nothing about variable y (and vice-versa) Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 19 Independence • If two variables x and y are independent then variable x tells us nothing about variable y (and vice-versa) Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 20 Independence • When variables are independent, the joint factorizes into a product of the marginals: Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 21 Probabilistic graphical models 22 What is a graphical model ? A graphical model is a way of representing probabilistic relationships between random variables. Variables are represented by nodes: Conditional (in)dependencies are represented by (missing) edges: Undirected edges simply give correlations between variables (Markov Random Field or Undirected Graphical model): Directed edges give causality relationships (Bayesian Network or Directed Graphical Model): Graphical Models • e.g. Bayesian networks, Bayes nets, Belief nets, Markov networks, HMMs, Dynamic Bayes nets, etc. • Themes: – representation – reasoning – learning • Original PGM reference book by Nir Friedman and Daphne Koller. Motivation for PGMs • Let X1,…,Xp be discrete random variables • Let P be a joint distribution over X1,…,Xp If the variables are binary, then we need O(2p) parameters to describe P Can we do better? • Key idea: use properties of independence Motivation for PGMs (2) • Two variables X and Y are independent if – P(X = x|Y = y) = P(X = x) for all values x, y – That is, learning the values of Y does not change prediction of X • If X and Y are independent then – P(X,Y) = P(X|Y)P(Y) = P(X)P(Y) • In general, if X1,…,Xp are independent, then – P(X1,…,Xp)= P(X1)...P(Xp) – Requires O(n) parameters Motivation for PGMs (3) • Unfortunately, most of random variables of interest are not independent of each other • A more suitable notion is that of conditional independence • Two variables X and Y are conditionally independent given Z if – P(X = x|Y = y,Z=z) = P(X = x|Z=z) for all values x,y,z – That is, learning the values of Y does not change prediction of X once we know the value of Z In summary…. Probability theory provides the glue whereby the parts are combined, ensuring that the system as a whole is consistent, and providing ways to interface models to data. The graph theoretic side of graphical models provides both an intuitively appealing interface by which humans can model highlyinteracting sets of variables as well as a data structure that lends itself naturally to the design of efficient general-purpose algorithms. Many of the classical multivariate probabilistic systems studied in fields such as statistics, systems engineering, information theory, pattern recognition and statistical mechanics are special cases of the general graphical model formalism -- examples include mixture models, factor analysis, hidden Markov models, and Kalman filters. Slide Credits • Simon Prince – Computer vision, models and learning 29 Next class • More on Graphical models • Readings for next lecture: – Lecture notes to be posted • Readings for today: – Lecture notes to be posted 30 Questions 31

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