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Dear all,</div>
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Apologies for my awful copying and pasting skills.</div>
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This Friday at 2 PM Dr Bettina Grün will talk about "Clustering with Bayesian Finite Mixture Models."</div>
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Many thanks to Dankmar who kindly pointed out the mistake!<br>
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Kind regards,</div>
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Jessie</div>
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P.S. The information is correct on Twitter, all-stat, Google calendar and MS Teams.</div>
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<span style="color: rgb(117, 123, 128);">---</span></p>
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<span style="margin: 0px; color: rgb(117, 123, 128);">Chieh-Hsi (Jessie) Wu, PhD</span></p>
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<span style="margin: 0px; color: rgb(117, 123, 128);">Lecturer in Statistics</span></p>
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<span style="color: rgb(117, 123, 128);">School of Mathematics</span></p>
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<span style="margin: 0px; color: rgb(117, 123, 128);">University of Southampton</span></p>
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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" style="font-size:11pt" color="#000000"><b>From:</b> Chieh-Hsi Wu <C-H.Wu@soton.ac.uk><br>
<b>Sent:</b> 11 October 2021 13:41<br>
<b>To:</b> S3ri-all@lists.soton.ac.uk <S3ri-all@lists.soton.ac.uk>; Socsci-staff-academic@lists.soton.ac.uk <Socsci-staff-academic@lists.soton.ac.uk>; socsci-phd-all@lists.soton.ac.uk <socsci-phd-all@lists.soton.ac.uk>; maths-stats-lists.soton.ac.uk-aliases
<maths-stats@lists.soton.ac.uk>; Rong Peng <rp2e13@soton.ac.uk>; Socsci-ec-all@lists.soton.ac.uk <Socsci-ec-all@lists.soton.ac.uk>; James Hall <J.E.Hall@soton.ac.uk>; maths-stats-pgr@all.soton.ac.uk <maths-stats-pgr@all.soton.ac.uk>; Orouba Almilaji <oalmilaji@bournemouth.ac.uk>;
David Culliford <D.J.Culliford@soton.ac.uk>; Christel de Bruijn <Christel.DeBruijn@bcu.ac.uk>; Turing-Southampton@ecs.soton.ac.uk <Turing-Southampton@ecs.soton.ac.uk>; Leo Westbury <lw@mrc.soton.ac.uk>; Faridah Alruwaili <F.Alruwaili@soton.ac.uk>; Peter Thomas
<PThomas@bournemouth.ac.uk>; Giuseppe Di Nardo <giuseppe.dinardo@hotmail.it>; Aditya Sivanand <A.Sivanand@soton.ac.uk>; Adetola Adediran <A.A.Adediran@soton.ac.uk><br>
<b>Subject:</b> S3RI seminar: Bettina Grün (Vienna University of Economics and Business), Friday 15 October</font>
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<span style="margin:0px; font-size:12pt; color:black; background-color:white"><span style="margin:0px; font-family:Calibri,Helvetica,sans-serif; font-size:12pt">Dear all,</span></span></div>
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<div style="margin:0px; background-color:white; color:black"><span style="margin:0px; font-family:Calibri,Helvetica,sans-serif; font-size:12pt">Great to see many of you at the seminar last week. </span></div>
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<div style="margin:0px; background-color:white; color:black"><span style="margin:0px; font-family:Calibri,Helvetica,sans-serif; font-size:12pt">At the S3RI seminar this *Friday* (15 October) at 2 PM, we have Dr Bettina Grün (Vienna University of Economics and
Business) giving a talk on</span></div>
<div style="margin:0px; background-color:white; color:black"><span style="margin:0px; font-family:Calibri,Helvetica,sans-serif"><br>
</span></div>
<div style="margin:0px; background-color:white; color:black"><span style="margin:0px; font-family:Calibri,Helvetica,sans-serif; font-size:12pt">"Variational Bayes for high-dimensional linear regression with sparse priors."</span></div>
<div style="margin:0px; background-color:white; color:black"><br>
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<div style="margin:0px; background-color:white; color:black">Please note that this seminar is scheduled outside the standard slot, and p<span style="font-family:Calibri,Helvetica,sans-serif; font-size:12pt">lease see below for the details of the talk and how
to join the seminar online.</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"> </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">Have a good week, and hope to see you at the seminar on Friday!</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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</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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<div class="x_x_copy-paste-block" style="margin:0px"><strong><em><span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">Clustering with Bayesian Finite Mixture Models</span></em></strong><br style="font-size:14.6667px">
<span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">Bettina Grün (Vienna University of Economics and Business)</span></div>
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<div class="x_x_copy-paste-block" style="margin:0px"><span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">Finite mixture models are a useful statistical model class for clustering </span><span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">and
density approximation. In the Bayesian framework, finite mixture models </span><span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">require the specification of suitable priors in addition to the data model. </span></div>
<div class="x_x_copy-paste-block" style="margin:0px"><span style="margin:0px; font-size:14.6667px"><br>
</span></div>
<div class="x_x_copy-paste-block" style="margin:0px"><span style="margin:0px; font-size:14.6667px"></span><span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">These priors allow to avoid spurious results and provide a principled
way </span><span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">to define cluster shapes and a preference for specific cluster solutions. A </span><span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">generic
model estimation scheme for finite mixtures with a fixed number of </span><span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">components is available using Markov chain Monte Carlo sampling with data </span><span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">augmentation.
The posterior allows to assess uncertainty in a comprehensive </span><span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">way, but component-specific posterior inference requires resolving the </span><span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">label
switching issue. Focusing on the application of Bayesian finite </span><span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">mixture models for clustering, we first discuss suitable specifications,</span><br style="font-size:14.6667px">
<span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">estimation and inference of the model if the number of components is </span><span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">assumed to be known.
We then continue to explain suitable strategies for </span><span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">fitting Bayesian finite mixture models when the number of components is not </span><span style="margin:0px; font-size:12pt; font-family:Calibri,Helvetica,sans-serif">known
considering also recent advances.</span></div>
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