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<p class="xxxmsonormal"><span lang="EN-US" style="font-size:14.0pt">Welcome to a Machine Learning Seminar on Wednesday, May 10 at 15:15 in Alan Turing (note the place)<o:p></o:p></span></p>
<p class="xxxmsonormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p style="margin:0cm;background:white"><b><span lang="EN-US" style="font-size:16.0pt;color:black">On the moment representation of stochastic filtering</span></b><b><span lang="EN-US" style="font-size:14.0pt;color:black"><br>
</span></b><span class="contentpasted0"><span lang="EN-US" style="font-size:14.0pt;color:black">Zheng Zhao, Uppsala University </span></span><span class="contentpasted0"><span style="font-size:14.0pt;color:black"><a href="https://zz.zabemon.com/"><span lang="EN-US">https://zz.zabemon.com/</span></a></span></span><span lang="EN-US" style="font-size:14.0pt;color:black"><o:p></o:p></span></p>
<p class="xxxmsonormal"><b><span lang="EN-US" style="font-size:8.5pt;font-family:"Verdana",sans-serif;color:black"> </span></b><span lang="EN-US"><o:p></o:p></span></p>
<p style="margin:0cm;background:white"><i><span lang="EN-US" style="color:black">Abstract:</span></i><span class="MsoHyperlinkFollowed"><span lang="EN-US" style="font-size:12.0pt;color:black;text-decoration:none">
</span></span><span class="contentpasted0"><span lang="EN-US" style="font-size:12.0pt;color:black">Stochastic filters are an important algorithms for estimating the distributions of latent variables conditioned on the observations. In reality, the filtering
distributions are hard to compute, hence, we often resort to approximate representations of the distributions that are easy to compute, for example, samples or Gaussian approximations. In this talk, we introduce a representation based on a sequence of moments.
We show that this representation is convergent in distribution as we increase the order of moments, and that the moments are easy to compute by using a numerical quadrature method. Furthermore, we numerically show that the performance of the moment filter
is comparable to standard particle filters in terms of convergence, parameter estimation, and computation. Please feel free to take a look at our implementation of the filter at </span></span><span class="contentpasted0"><span style="font-size:12.0pt;color:black"><a href="https://github.com/zgbkdlm/mfs"><span lang="EN-US">https://github.com/zgbkdlm/mfs</span></a></span></span><span class="contentpasted0"><span lang="EN-US" style="font-size:12.0pt;color:black">. <o:p></o:p></span></span></p>
<p style="margin:0cm;background:white"><span lang="EN-US" style="color:black"><o:p> </o:p></span></p>
<p class="xxxmsonormal"><span lang="EN-US">Location: <b>Alan Turing,</b> <a href="https://www.ida.liu.se/department/location/search.en.shtml?keyword=alan">
https://www.ida.liu.se/department/location/search.en.shtml?keyword=alan</a><o:p></o:p></span></p>
<p class="xxxmsonormal">Organizers: Fredrik Lindsten, Sourabh Balgi<o:p></o:p></p>
<p class="xxxmsonormal"> <o:p></o:p></p>
<p class="xxxmsonormal">------------------<o:p></o:p></p>
<p class="xxxmsonormal"> <o:p></o:p></p>
<p class="xxxmsonormal"><span lang="EN-US">The list of future seminars in the series is available at:
</span><a href="http://www.ida.liu.se/research/machinelearning/seminars/"><span lang="EN-US">http://www.ida.liu.se/research/machinelearning/seminars/</span></a><span lang="EN-US"><o:p></o:p></span></p>
<p class="xxxmsonormal"><span lang="EN-US">You can subscribe to the seminar series' calendar using this ics link:
<a href="https://outlook.office365.com/owa/calendar/4d811ae47ce446f58d11a7c2f50a7ed8@ad.liu.se/0f5253d7bc7841248c71eb4c28eb2d668927992292494627279/calendar.ics">
https://outlook.office365.com/owa/calendar/4d811ae47ce446f58d11a7c2f50a7ed8@ad.liu.se/0f5253d7bc7841248c71eb4c28eb2d668927992292494627279/calendar.ics</a><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
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