Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming

arXiv:2306.02568v2 Announce Type: replace-cross
Abstract: We propose the stochastic optimal path which solves the classical optimal path problem by a probability-softening solution. This unified approach transforms a wide range of DP problems into directed acyclic graphs in which all paths follow a Gibbs distribution. We show the equivalence of the Gibbs distribution to a message-passing algorithm by the properties of the Gumbel distribution and give all the ingredients required for variational Bayesian inference of a latent path, namely Bayesian dynamic …

abstract arxiv bayesian cs.lg distribution dynamic gibbs graphs path probability programming propagation show solution stat.ml stochastic type