Simulating from Mixture Distributions

Giovanni Petris and Luca Tardella

Abstract:

We present a method of generating a random vector from a distribution having an absolutely continuous component and a discrete component. The method is then extended to more general mixture distributions that arise quite naturally when testing nested hypotheses within a Bayesian framework. The main idea is to transform the mixture distribution of interest in an absolutely continuous one, in a way that does not require the explicit calculation of the relative weights of the various components of the mixture.

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