Probability - Caltech

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Probability - Caltech

Probability - Caltech

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Posterior inference ! Suppose we know: ! Prior probability P(C) ! Likelihood P(T | C) .1 .9 .9 .1 .01 .99 ! How do we get 23

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Example: Condi)onal distribu)ons What is P(Toothache|cavity)? Joint distribu)on 22

Posterior inference ! Suppose we know: ! Prior probability P(C) ! Likelihood P(T | C) .1 .9 .9 .1 .01 .99 ! How do we get 23

Page 1 and 2: Introduc)on to Ar)ficial Intellig
Page 3 and 4: Announcements ! Grading: AvianAske
Page 5 and 6: Quan)fying Uncertainty ! So far,
Page 7 and 8: Decision making under uncertaint
Page 9 and 10: Why use probabili)es? Related ev
Page 11 and 12: Interpreta)on of probabili)es ! P
Page 13 and 14: Examples ! Bernoulli distribu)on:
Page 15 and 16: Example: Gaussian distribu)on !
Page 17 and 18: Example: Mul)variate Gaussian 17
Page 19 and 20: Marginal distribu)ons What is P(
Page 21: Condi)onal distribu)ons ! Formal
Page 25 and 26: Independent RVs ! What if RVs a