Lecture 20 | Bayesian analysis

STATS 200: Introduction to Statistical InferenceAutumn 2016 Lecture 20 Bayesian analysisOur treatment of parameter estimation thus far has assumed that is an unknown butnon-random quantity it is some fixed parameter describing the true distribution of data,and our goal was to determine this parameter. This is the called thefrequentistparadigmof statistical inference. In this and the next Lecture , we will describe an alternativeBayesianparadigm, in which itself is modeled as a random variable. The Bayesian paradigm natu-rally incorporates our prior belief about the unknown parameter , and updates this beliefbased on observed Prior and posterior distributionsRecall that ifX,Yare two random variables having joint PDF or PMFfX,Y(x,y), then themarginal distributionofXis given by the PDFfX(x) = fX,Y(x,y)dyin the continuous case and by the PMFfX(x) = yfX,Y(x,y)in the discrete case; this describes the probability distribution ofXalone. TheconditionaldistributionofYgivenX=xis defined by the PDF or PMFfY|X(y|x) =fX,Y(x,y)fX(x),and represents the probability distribution ofYif it is known thatX=x.

Lecture 20 | Bayesian analysis Our treatment of parameter estimation thus far has assumed that is an unknown but non-random quantity|it is some xed parameter describing the true distribution of data, and our goal was to determine this parameter. This is the called the frequentist paradigm of statistical inference.

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