Bayesian Missing Data Problems (2010)

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Overview of Bayesian Missing Data Problems (2010) PDF free download:

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Features of Bayesian Missing Data Problems (2010) PDF free download:

    The expectation-maximization (EM) method, the data augmentation (DA) method, together with their improved versions are well-known techniques for handling missing data problems (MDPs). The DA method consists of two parts: (a) the DA structure of three constituents, namely a set of parameters, a set of observable data and a set of latent variables whose values are not available, and (b) the DA algorithm(s) for finding the observed posterior density of parameter or for sampling from observed posterior distribution. With regard to practical posterior computation in Bayesian analysis of MDPs, including the calculation of posterior densities or intervals, the last 20 years or so have seen the proliferation of methods based on iterative sampling until convergence, such as Gibbs and other Markov chain Monte Carlo (MCMC) sampling.

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