Bayesian modelling and inference difficulties on mixtures of distributions
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چکیده
Today’s data analysts and modellers are in the luxurious position of being able to more closely describe, estimate, predict and infer about complex systems of interest, thanks to ever more powerful computational methods but also wider ranges of modelling distributions. Mixture models constitute a fascinating illustration of these aspects: while within a parametric family, they offer malleable approximations in non-parametric settings; although based on standard distributions, they pose highly complex computational challenges; and they are both easy to constrain to meet identifiability requirements and fall within the class of ill-posed problems. This note aims to introduce the reader to Bayesian modelling and inference difficulties on mixtures of distributions.
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تاریخ انتشار 2004