Parallel Importance Sampling in Conditional Linear Gaussian Networks
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Salmerón Cerdán, Antonio; Ramos López, Darío; Borchani, Hanen; Martínez, Ana M.; Masegosa, Andrés R.; [et al.]Date
2015Abstract
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.