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Dear all,</div>
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Hope everyone is having a nice week!</div>
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Just a reminder, <span style="margin: 0px; font-size: 12pt; color: black; background-color: white;"><span style="margin:0px;font-family:Calibri, Helvetica, sans-serif">we have Dr<span> </span><span data-markjs="true" class="mark4zsj36q0q" style="margin:0px">Kolyan</span><span> </span>Ray
(Imperial College London) speaking at the S3RI seminar <span style="color: rgb(0, 0, 0); background-color: rgb(255, 255, 255); display: inline !important;">
today </span>at 2 PM on</span></span>
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<span style="margin:0px;font-family:Calibri, Helvetica, sans-serif">"Variational Bayes for high-dimensional linear regression with sparse priors."</span></div>
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<p style="background-color: white; margin-top: 0px; margin-bottom: 0px;"><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: black;">Please see below for the details of the talk and how to
join the seminar online.</span><span style="margin: 0px; font-size: 12pt; font-family: "Times New Roman", serif; color: black;"></span></p>
<p style="background-color: white; margin-top: 0px; margin-bottom: 0px;"><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: black;"> </span><span style="margin: 0px; font-size: 12pt; font-family: "Times New Roman", serif; color: black;"></span></p>
<p style="background-color: white; margin-top: 0px; margin-bottom: 0px;"><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: black;">Hope to see you at the seminar later at 2 PM.</span><span style="margin: 0px; font-size: 12pt; font-family: "Times New Roman", serif; color: black;"></span></p>
<p style="background-color: white; margin-top: 0px; margin-bottom: 0px;"><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: black;"> </span><span style="margin: 0px; font-size: 12pt; font-family: "Times New Roman", serif; color: black;"></span></p>
<p style="background-color: white; margin-top: 0px; margin-bottom: 0px;"><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: black;">Kind regards,</span><span style="margin: 0px; font-size: 12pt; font-family: "Times New Roman", serif; color: black;"></span></p>
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<p style="background-color: white; margin-top: 0px; margin-bottom: 0px;"><span style="margin: 0px; font-size: 11.5pt; color: rgb(32, 31, 30); background-color: white;"><br>
</span><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: black;">Jessie</span></p>
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<span style="margin: 0px; font-size: 15px; font-family: Calibri, Arial, Helvetica, sans-serif; background-color: white; color: black;"><span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif"><b><em><strong><span style="color: rgb(0, 0, 0); font-family: Calibri, Helvetica, sans-serif; font-size: 12pt; background-color: rgb(255, 255, 255); display: inline !important;"></span></strong></em></b></span></span></span></p>
<div><span style="font-size:12pt"><strong><em>Variational Bayes for high-dimensional linear regression with sparse priors</em></strong></span>
<div style="font-size:12pt"><span style="color: rgb(0, 0, 0); font-family: Calibri, Helvetica, sans-serif; background-color: rgb(255, 255, 255); display: inline !important;">Kolyan Ray (Imperial College London</span>)</div>
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<div style="font-size:12pt">A core problem in Bayesian statistics is approximating difficult to compute posterior distributions. In variational Bayes (VB), a method from machine learning, one approximates the posterior through optimization, which is typically
faster than Markov chain Monte Carlo. We study a mean-field (i.e. factorizable) VB approximation to Bayesian model selection priors, including the popular spike-and-slab prior, in sparse high-dimensional linear regression. We establish convergence rates for
this VB approach, studying conditions under which it provides good estimation. We also discuss computational issues and study the empirical performance of the algorithm.</div>
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<span style="margin: 0px; font-family: Calibri, Helvetica, sans-serif; background-color: white; letter-spacing: 0.15pt; color: black; font-size: 12pt;"><Details for jointing S3RI team on Microsoft Teams></span></p>
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<span style="margin: 0px; font-family: Calibri, Helvetica, sans-serif; background-color: white; letter-spacing: 0.15pt; color: black; font-size: 12pt;">If you would like to join the S3RI team on Microsoft Teams, please use the link below:</span><span style="margin: 0px; font-size: 10.5pt; font-family: Arial, sans-serif; color: rgb(60, 64, 67); background-color: white; letter-spacing: 0.15pt;"><br>
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