Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs

arXiv:2405.01737v1 Announce Type: cross
Abstract: Likelihood-free inference methods based on neural conditional density estimation were shown to drastically reduce the simulation burden in comparison to classical methods such as ABC. When applied in the context of any latent variable model, such as a Hidden Markov model (HMM), these methods are designed to only estimate the parameters, rather than the joint distribution of the parameters and the hidden states. Naive application of these methods to a HMM, ignoring the inference of …

abstract arxiv bayesian bayesian inference comparison context cs.lg free hidden inference latent variable model likelihood markov reduce sample simulation stat.co stat.ml the simulation type