Probability - Caltech

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Condi)onal distribu)ons ! Condi)onal (or posterior) probabili)es. E.g., ! Condi)onal distribu)ons specify values for all state combina)ons ! New evidence can change posterior belief 20
Condi)onal distribu)ons ! Formal defini)on: = ! Product rule ! For distribu)ons: (set of equa)ons, one for each instan)a)on of A,B) ! Chain rule: 21
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: Marginal distribu)ons What is P(
Page 23 and 24: Posterior inference ! Suppose we
Page 25 and 26: Independent RVs ! What if RVs a

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