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Frank van der Meulen - Automatic Backward Filtering Forward Guiding for Markov processes and graphical models
I discuss a structured way for efficient inference in probabilistic graphical models with building blocks consisting of Markovian stochastic processes. The starting point is a generative model, a forward description of the probabilistic dynamics. The information provided by observations can be backpropagated through the model to transform the generative (forward) model into a conditional model guided by the data. It approximates the actual conditional model with known likelihood-ratio between the two. The backward filter and the forward change of measure are suitable to be incorporated into a probabilistic programming context because they can be formulated as a set of transformation rules. The guided generative model can be combined with different approaches to efficiently sample latent states and parameters conditional on observations. Application settings include Markov chains with discrete state space, interacting particle systems, state space models, branching diffusions and Gamma processes.

Apr 21, 2021 05:00 PM in Paris

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