3,178 research outputs found
Answering queries in hybrid Bayesian networks using importance sampling
In this paper we propose an algorithm for answering queries in hybrid Bayesian networks where the underlying probability distribution is of class MTE (mixture of truncated exponentials). The algorithm is based on importance sampling simulation. We show how, like existing importance sampling algorithms for discrete networks, it is able to provide answers to multiple queries simultaneously using a single sample. The behaviour of the new algorithm is experimentally tested and compared with previous methods existing in the literature
Parallel importance sampling in conditional linear Gaussian networks
In this paper we analyse the problem of probabilistic inference in CLG networks when evidence comes in streams. In such situations, fast and scalable algorithms, able to provide accurate responses in
a short time are required. We consider the instantiation of variational
inference and importance sampling, two well known tools for probabilistic inference, to the CLG case. The experimental results over synthetic
networks show how a parallel version importance sampling, and more
precisely evidence weighting, is a promising scheme, as it is accurate and
scales up with respect to available computing resources
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