Model Averaging (Bayesian Learning)

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1 Model Averaging (Bayesian Learning) We want to predict the output Y of a new case that has input X = x given the training examples e: p(y x e) = m M P(Y m x e) = m M P(Y m x e)p(m x e) = m M P(Y m x)p(m e) M is a set of mutually exclusive and covering hypotheses. What assumptions are made here? c D. Poole and A. Mackworth 2010 Artificial Intelligence, Lecture 7.6, Page 1

2 Learning Under Uncertainty The posterior probability of a model given examples e: P(m e) = P(e m) P(m) P(e) The likelihood, P(e m), is the probability that model m would have produced examples e. The prior, P(m), encodes the learning bias P(e) is a normalizing constant so the probabilities of the models sum to 1. Examples e = {e 1,..., e k } are independent and identically distributed (i.i.d.) given m if P(e m) = k P(e i m) i=1 c D. Poole and A. Mackworth 2010 Artificial Intelligence, Lecture 7.6, Page 2

3 Plate Notation m m e 1 e 2... e e k i i c D. Poole and A. Mackworth 2010 Artificial Intelligence, Lecture 7.6, Page 3

4 Bayesian Leaning of Probabilities Y has two outcomes y and y. We want the probability of y given training examples e. We can treat the probability of y as a real-valued random variable on the interval [0, 1], called φ. Bayes rule gives: P(φ=p e) = c D. Poole and A. Mackworth 2010 Artificial Intelligence, Lecture 7.6, Page 4

5 Bayesian Leaning of Probabilities Y has two outcomes y and y. We want the probability of y given training examples e. We can treat the probability of y as a real-valued random variable on the interval [0, 1], called φ. Bayes rule gives: P(φ=p e) = P(e φ=p) P(φ=p) P(e) Suppose e is a sequence of n 1 instances of y and n 0 instances of y: P(e φ=p) = c D. Poole and A. Mackworth 2010 Artificial Intelligence, Lecture 7.6, Page 5

6 Bayesian Leaning of Probabilities Y has two outcomes y and y. We want the probability of y given training examples e. We can treat the probability of y as a real-valued random variable on the interval [0, 1], called φ. Bayes rule gives: P(φ=p e) = P(e φ=p) P(φ=p) P(e) Suppose e is a sequence of n 1 instances of y and n 0 instances of y: P(e φ=p) = p n 1 (1 p) n 0 Uniform prior: P(φ=p) = 1 for all p [0, 1]. c D. Poole and A. Mackworth 2010 Artificial Intelligence, Lecture 7.6, Page 6

7 Posterior Probabilities for Different Training Examples (beta distribution) n 0 =0, n 1 =0 n 0 =1, n 1 =2 n 0 =2, n 1 =4 n 0 =4, n 1 =8 2 P(φ e) φ c D. Poole and A. Mackworth 2010 Artificial Intelligence, Lecture 7.6, Page 7

8 MAP model The maximum a posteriori probability (MAP) model is the model m that maximizes P(m e). That is, it maximizes: P(e m) P(m) Thus it minimizes: ( log P(e m)) + ( log P(m)) which is the number of bits to send the examples, e, given the model m plus the number of bits to send the model m. c D. Poole and A. Mackworth 2010 Artificial Intelligence, Lecture 7.6, Page 8

9 Averaging Over Models Idea: Rather than choosing the most likely model, average over all models, weighted by their posterior probabilities given the examples. If you have observed a sequence of n 1 instances of y and n 0 instances of y, with uniform prior: the most likely value (MAP) is n 1 n 0 + n 1 the expected value is n n 0 + n c D. Poole and A. Mackworth 2010 Artificial Intelligence, Lecture 7.6, Page 9

10 Beta Distribution Beta α 0,α 1 (p) = 1 K pα 1 1 (1 p) α 0 1 where K is a normalizing constant. α i > 0. The uniform distribution on [0, 1] is Beta 1,1. The expected value is α 1 /(α 0 + α 1 ). If the prior probability of a Boolean variable is Beta α 0,α 1, the posterior distribution after observing n 1 true cases and n 0 false cases is: c D. Poole and A. Mackworth 2010 Artificial Intelligence, Lecture 7.6, Page 10

11 Beta Distribution Beta α 0,α 1 (p) = 1 K pα 1 1 (1 p) α 0 1 where K is a normalizing constant. α i > 0. The uniform distribution on [0, 1] is Beta 1,1. The expected value is α 1 /(α 0 + α 1 ). If the prior probability of a Boolean variable is Beta α 0,α 1, the posterior distribution after observing n 1 true cases and n 0 false cases is: Beta α 0+n 0,α 1 +n 1 c D. Poole and A. Mackworth 2010 Artificial Intelligence, Lecture 7.6, Page 11

12 Dirichlet distribution Suppose Y has k values. The Dirichlet distribution has two sorts of parameters, positive counts α1,..., α k α i is one more than the count of the ith outcome. probability parameters p1,..., p k p i is the probability of the ith outcome Dirichlet α 1,...,α k (p 1,..., p k ) = 1 k p α j 1 j K where K is a normalizing constant The expected value of ith outcome is α i j α j j=1 c D. Poole and A. Mackworth 2010 Artificial Intelligence, Lecture 7.6, Page 12

13 Hierarchical Bayesian Model Where do the priors come from? Example: S XH is true when patient X is sick in hospital H. We want to learn the probability of Sick for each hospital. Where do the prior probabilities for the hospitals come from? α 1 α 2 α 1 α 2 φ H φ 1 φ 2... φ k S XH X H S 11 S 12 S 21 S 22 S 1k (a) (b) c D. Poole and A. Mackworth 2010 Artificial Intelligence, Lecture 7.6, Page 13

Learning Objectives. c D. Poole and A. Mackworth 2010 Artificial Intelligence, Lecture 7.2, Page 1

Learning Objectives. c D. Poole and A. Mackworth 2010 Artificial Intelligence, Lecture 7.2, Page 1 Learning Objectives At the end of the class you should be able to: identify a supervised learning problem characterize how the prediction is a function of the error measure avoid mixing the training and