Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks
Journal article, Peer reviewed
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Original versionInternational Journal of Approximate Reasoning. 2018, 100 115-134. 10.1016/j.ijar.2018.06.004
In this paper we propose a scalable importance sampling algorithm for computing Gaussian mixture posteriors in conditional linear Gaussian Bayesian networks. Our contribution is based on using a stochastic gradient ascent procedure taking as input a stream of importance sampling weights, so that a mixture of Gaussians is dynamically updated with no need to store the full sample. The algorithm has been designed following a Map/Reduce approach and is therefore scalable with respect to computing resources. The implementation of the proposed algorithm is available as part of the AMIDST open-source toolbox for scalable probabilistic machine learning (http://www.amidsttoolbox.com).