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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>John Winn</author_name><author_url>https://www.microsoft.com/en-us/research/people/jwinn/</author_url><title>Variational Message Passing - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="3gxqynM0VV"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/variational-message-passing/"&gt;Variational Message Passing&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/variational-message-passing/embed/#?secret=3gxqynM0VV" width="600" height="338" title="&#x201C;Variational Message Passing&#x201D; &#x2014; Microsoft Research" data-secret="3gxqynM0VV" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>This paper presents Variational Message Passing (VMP), a general purpose algorithm for applying variational inference to a Bayesian Network. Like belief propagation, Variational Message Passing proceeds by passing messages between nodes in the graph and updating posterior beliefs using local operations at each node. Each such update increases a lower bound on the log evidence [&hellip;]</description></oembed>
