Abstract
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).
Originalsprog | Engelsk |
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Artikelnummer | 100 |
Tidsskrift | International Journal of Approximate Reasoning |
Vol/bind | 100 |
Sider (fra-til) | 115-134 |
Antal sider | 20 |
ISSN | 0888-613X |
DOI | |
Status | Udgivet - 1 sep. 2018 |