{"id":150198,"date":"2004-01-01T00:00:00","date_gmt":"2004-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/power-ep\/"},"modified":"2018-10-16T21:29:05","modified_gmt":"2018-10-17T04:29:05","slug":"power-ep","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/power-ep\/","title":{"rendered":"Power EP"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This note describes power EP, an extension of Expectation Propagation (EP) that makes the computations more tractable. In this way, power EP is applicable to a wide variety of models, much more than EP. Instead of minimizing KL-divergence at each step, power EP minimizes \u03b1-divergence. This minimization turns out to be equivalent to minimizing KL-divergence with the exact distribution raised to a power. By choosing this power to cancel exponents, the problem may be substantially simplified. The resulting approximation is not the same as regular EP, but in practice is still very good, and allows tackling problems which are intractable under regular EP.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This note describes power EP, an extension of Expectation Propagation (EP) that makes the computations more tractable. In this way, power EP is applicable to a wide variety of models, much more than EP. Instead of minimizing KL-divergence at each step, power EP minimizes \u03b1-divergence. This minimization turns out to be equivalent to minimizing KL-divergence 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