Recursive computing for Markov random fields
نویسنده
چکیده
We present a recursive algorithm to compute a collection of normalising constants which can be used in a straightforward manner to sample a realisation from a Markov random field. Further we present important consequences of this result which renders possible tasks such as maximising Markov random fields, computing marginal distributions, exact inference for certain loss functions and computing marginal likelihoods. Some key words: Autologistic distribution; exact sampling; hidden Markov random field; Ising model; normalising constant.
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