Neural Adaptive Sequential Monte Carlo Supplementary Material
نویسندگان
چکیده
This section reviews the basic SMC algorithm, beginning by recapitulating the setup described in the main text. Consider a probabilistic model comprising (possibly multi-dimensional) hidden and observed states z1:T and x1:T respectively, whose joint distribution factorizes as p(z1:T ,x1:T ) = p(z1)p(x1|z1) ∏T t=2 p(zt|z1:t−1)p(xt|z1:t,x1:t−1). This general form subsumes common statespace models, such as Hidden Markov Models (HMMs), as well as non-Markovian models for the hidden state, such as Gaussian processes.
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