It is probably fair to say that exact inference in graphical models is considered a solved problem, at least regarding its computational complexity: it is exponential in the treew...
Abstract. Dynamic Bayesian networks (DBNs) extend Bayesian networks from static domains to dynamic domains. The only known generic method for exact inference in DBNs is based on dy...
— Many inference problems that arise in sensor networks require the computation of a global conclusion that is consistent with local information known to each node. A large class...
This paper describes a parameter estimation method for multi-label classification that does not rely on approximate inference. It is known that multi-label classification involvin...
Rao–Blackwellization is an approximation technique for probabilistic inference that flexibly combines exact inference with sampling. It is useful in models where conditioning o...