Transcription of Chapter 9 The exponential family: Conjugate priors
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Chapter 9 The exponential family: Conjugate priors
Chapter 9 The exponential family: ConjugatepriorsWithin the bayesian framework the parameter is treated as a random quantity. Thisrequires us to specify aprior distributionp( ), from which we can obtain theposteriordistributionp( |x) via Bayes theorem:p( |x) =p(x| )p( )p(x),( )wherep(x| ) is the inferential conclusions obtained within the bayesian framework are based in one wayor another on averages computed under the posterior distribution , and thus for the Bayesianframework to be useful it is essential to be able to compute these integrals with some effectiveprocedure.
Most inferential conclusions obtained within the Bayesian framework are based in one way or another on averages computed under the posterior distribution, and thus for the Bayesian framework to be useful it is essential to be able to compute these integrals with …
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