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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>
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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">"Variational Bayes for high-dimensional linear regression with sparse priors."</span></div>
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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_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_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>
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<div class="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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