Metropolis-Hastings for Mixtures of Conditional Distributions

Models with embedded conditioning operations — especially with conditioning within conditional branches — are a challenge for Monte-Carlo Markov Chain (MCMC) inference. They are out of scope of the popular Wingate et al. algorithm or many of its variations. Computing the MCMC acceptance ratio in this case has been an open problem.

We demonstrate why we need such models. Second, we derive the acceptance ratio formula. The corresponding MH algorithm is implemented in the Hakaru10 system, which thus can handle mixtures of conditional distributions.

PPS2017

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