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X-WR-CALNAME;VALUE=TEXT:Statistics Colloquium: Yves Atchade (Boston University)
PRODID:-//Harvard events data//EN
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SUMMARY:Statistics Colloquium: Yves Atchade (Boston University)
DESCRIPTION:<h3>	<drupal-media data-entity-type="media" data-entity-uuid="bea9b2dc-382c-4b5c-86f1-494613adc11c" data-align="left" alt="Headshot of Yves Atchade" data-view-mode="hwp_small"></drupal-media><u>Title: </u></h3><p>	<span style='NewRoman";mso-bidi-theme-font:minor-bidi;mso-ansi-language:EN-US;mso-fareast-language:EN-US;mso-bidi-language:AR-SA'>A computational framework for large scale Bayesian inference</span></p><h3>	<u>Abstract:</u></h3><p>	<span style='NewRoman";mso-bidi-theme-font:minor-bidi;mso-ansi-language:EN-US;mso-fareast-language:EN-US;mso-bidi-language:AR-SA'>The talk deals with statistical models where the number of parameters in the model and/or the number of data points are very large. I will describe a simple spike-and-slab framework modeling strategy, and a novel approximate MCMC sampler to deal with the resulting posterior distribution. The approximate sampling algorithm is remarkably accurate as I will show with several examples. I will also describe some new results on the mixing times of the algorithm in the high-dimensional regime.</span></p>
LOCATION:Zoom - please contact emilie_campanelli@fas.harvard.edu for more information
STATUS:CONFIRMED
DTSTART:20201005T143000Z
DTEND:20201005T153000Z
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