I will describe Divide-and-Conquer Sequential Monte Carlo (D&C SMC), a method for performing inference on a collection of auxiliary distributions organized into a tree. In contrast to standard SMC samplers, D&C SMC exploits multiple populations of weighted particles, while still being an exact approximate method.
D&C SMC provides a simple method for approximating the posterior distribution of Bayesian MSA models. It is easy to parallelize, has attractive theoretical guarantees and is flexible on the model assumptions. I will provide some examples of Bayesian MSA models on which D&C SMC can be applied to obtain a distributed MSA approximation algorithm.
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