1 Machine Learning 10-701 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 18, 2011 Today: • Bayes Rule • Estimating parameters • maximum likelihood • max a posteriori Readings: Probability review • Bishop Ch. 1 thru 1.2.3 • Bishop, Ch. 2 thru 2.2 • Andrew Moore’s online tutorial many of these slides are derived from William Cohen, Andrew Moore, Aarti Singh, Eric Xing, Carlos Guestrin. - Thanks! Visualizing Probabilities Sample space of all possible worlds Its area is 1 B A A ^ B2 Definition of Conditional Probability P(A ^ B) P(A|B) = ----------- P(B) B A Definition of Conditional Probability P(A ^ B) P(A|B) = ----------- P(B) Corollary: The Chain Rule P(A ^ B) = P(A|B) P(B) P(C ^ A ^ B) = P(C|A ^ B) P(A|B) P(B)3 Independent Events • Definition: two events A and B are independent if P(A ^ B)=P(A)*P(B) • Intuition: knowing A tells us nothing about the value of B (and vice versa) Bayes Rule • let’s write 2 expressions for P(A ^ B) B A A ^ B4 P(B|A) * P(A) P(B) P(A|B) = Bayes, Thomas (1763) An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53:370-418 …by no means merely a curious speculation in the doctrine of chances, but necessary to be solved in order to a sure foundation for all our reasonings concerning past facts, and what is likely to be hereafter…. necessary to be considered by any that would give a clear account of the strength of analogical or inductive reasoning… Bayes’ rule we call P(A) the “prior” and P(A|B) the “posterior” Other Forms of Bayes Rule5 Applying Bayes Rule A = you have the flu, B = you just coughed Assume: P(A) = 0.05 P(B|A) = 0.80 P(B| ~A) = 0.2 what is P(flu | cough) = P(A|B)? what does all this have to do with function approximation?6 The Joint Distribution Recipe for making a joint distribution of M variables: Example: Boolean variables A, B, C A B C Prob 0 0 0 0.30 0 0 1 0.05 0 1 0 0.10 0 1 1 0.05 1 0 0 0.05 1 0 1 0.10 1 1 0 0.25 1 1 1 0.10 A B C 0.05 0.25 0.10 0.05 0.05 0.10 0.10 0.30 [A. Moore] The Joint Distribution Recipe for making a joint distribution of M variables: 1. Make a truth table listing all combinations of values of your variables (if there are M Boolean variables then the table will have 2M rows). Example: Boolean variables A, B, C A B C Prob 0 0 0 0.30 0 0 1 0.05 0 1 0 0.10 0 1 1 0.05 1 0 0 0.05 1 0 1 0.10 1 1 0 0.25 1 1 1 0.10 A B C 0.05 0.25 0.10 0.05 0.05 0.10 0.10 0.30 [A. Moore]7 The Joint Distribution Recipe for making a joint distribution of M variables: 1. Make a truth table listing all combinations of values of your variables (if there are M Boolean variables then the table will have 2M rows). 2. For each combination of values, say how probable it is. Example: Boolean variables A, B, C A B C Prob 0 0 0 0.30 0 0 1 0.05 0 1 0 0.10 0 1 1 0.05 1 0 0 0.05 1 0 1 0.10 1 1 0 0.25 1 1 1 0.10 A B C 0.05 0.25 0.10 0.05 0.05 0.10 0.10 0.30 [A. Moore] The Joint Distribution Recipe for making a joint distribution of M variables: 1. Make a truth table listing all combinations of values of your variables (if there are M Boolean variables then the table will have 2M rows). 2. For each combination of values, say how probable it is. 3. If you subscribe to the axioms of probability, those numbers must sum to 1. Example: Boolean variables A, B, C A B C Prob 0 0 0 0.30 0 0 1 0.05 0 1 0 0.10 0 1 1 0.05 1 0 0 0.05 1 0 1 0.10 1 1 0 0.25 1 1 1 0.10 A B C 0.05 0.25 0.10 0.05 0.05 0.10 0.10 0.30 [A. Moore]8 Using the Joint One you have the JD you can ask for the probability of any logical expression involving your attribute [A. Moore] Using the Joint P(Poor Male) = 0.4654 [A. Moore]9 Using the Joint P(Poor) = 0.7604 [A. Moore] Inference with the Joint P(Male | Poor) = 0.4654 / 0.7604 = 0.612 [A. Moore]10 Learning and the Joint Distribution Suppose we want to learn the function f: <G, H> W Equivalently, P(W | G, H) Solution: learn joint distribution from data, calculate P(W | G, H) e.g., P(W=rich | G = female, H = 40.5- ) = [A. Moore] sounds like the solution to learning F: X Y, or P(Y | X). Are we done?11 [C. Guestrin] [C. Guestrin]12 [C. Guestrin] Maximum Likelihood Estimate for Θ [C. Guestrin]13 [C. Guestrin] [C. Guestrin]14 [C. Guestrin] Beta prior distribution – P(θ) [C. Guestrin]15 Beta prior distribution – P(θ) [C. Guestrin] [C. Guestrin]16 [C. Guestrin] Conjugate priors [A. Singh]17 Conjugate priors [A. Singh] Estimating Parameters • Maximum Likelihood Estimate (MLE): choose θ that maximizes probability of observed data • Maximum a Posteriori (MAP) estimate: choose θ that is most probable given prior probability and the data18 Dirichlet distribution • number of heads in N flips of a two-sided coin – follows a binomial distribution – Beta is a good prior (conjugate prior for binomial) • what it’s not two-sided, but k-sided? – follows a multinomial distribution – Dirichlet distribution is the conjugate prior You should know • Probability basics – random variables, events, sample space, conditional probs, … – independence of random variables – Bayes rule – Joint probability distributions – calculating probabilities from the joint distribution • Estimating parameters from data – maximum likelihood estimates – maximum a posteriori estimates – distributions – binomial, Beta, Dirichlet, … – conjugate priors19 Extra slides Expected values Given discrete random variable X, the expected value of X, written E[X] is We also can talk about the expected value of functions of X20 Covariance Given two discrete r.v.’s X and Y, we define the covariance of X and Y as e.g., X=gender, Y=playsFootball or X=gender, Y=leftHanded
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