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Inference for binary Markov random fields without tears (or MCMC).

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If you have a question about this talk, please contact Nikolaos Demiris.

Binary Markov random fields have played an important role in the development of MCMC methods. The Metropolis algorithm was designed to sample from the Ising model, for example. In this talk we will present an non-MCMC based approach to carrying out inference for such models. Here I will present an algorithm which allows, for example, direct sampling from the Markov random field, computation of marginal distributions of lattice points, but crucially which also allows inference for the parameters of the MRF . These algorithms can be carried out exactly if the dimension of the lattice is of moderate size. We present an approximate inference scheme for the case where the lattice is of large dimension. This is joint work with Havard Rue (NTNU, Trondheim).

This talk is part of the MRC Biostatistics Unit Seminars series.

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