<div dir="ltr">Caros colegas,<div><br></div><div>A pedido da professora Alexandra Schmidt da McGill University, segue abaixo a divulgação de Webinar a ser ministrad<font face="arial, sans-serif">o pela professora <span style="color:rgb(0,0,0)">Marina Vannucci (R</span></font>ice University)<span style="font-family:arial,sans-serif"> no dia 06 de março.</span></div><div><br></div><div>Obrigada.</div><div><br></div><div>Atenciosamente,</div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">--------------------------------------------------------</div><div class="msg-4283395223035608141"><div dir="ltr"><div dir="ltr" id="m_870947022011003356divRplyFwdMsg"><div> </div>
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<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">Dear Colleagues,</span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"><br>
</span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">We are excited to announce our upcoming CIRS/SWB
Webinar titled <b>“Bayesian Variable Selection”</b> by <b>Professor Marina Vannucci</b> to take place on
<b>Wedensday, March 6, 2024, 12-1:30 PM ET.</b></span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"> </span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">Our webinar series, sponsored jointly by the American
Statistical Association’s Committee on International Relations in Statistics (CIRS) and by Statistics Without Borders (SWB), provides introductory lectures by experts on important topics of current interest, and is aimed at an international audience. </span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">Please join us by registering for this webinar and
<b>help us spread the word by forwarding this to colleagues you think might be interested</b>. Participation is free but registration is required (<a href="https://amstat.zoom.us/webinar/register/WN_lYW5bi3HTs6GKoqES5_T9w#/registration" id="m_870947022011003356OWAcfd2065d-7604-3e75-cd3d-56afd7200c15" title="Original URL: https://amstat.zoom.us/webinar/register/WN_lYW5bi3HTs6GKoqES5_T9w#/registration. Click or tap if you trust this link." style="margin-top:0px;margin-bottom:0px" target="_blank">register
here</a>). </span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"><br>
</span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)">We look forward to seeing you there!</span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"> </span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"><b>Tile:
</b>Bayesian Variable Selection</span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"> </span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"><b>Speaker:
</b>Dr. Marina Vannucci</span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"> </span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"><b>Abstract</b>:</span><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(33,37,41)"> Bayesian
variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets. Identifying relevant variables to include in a model allows simpler interpretation, avoids overfitting and multicollinearity, and
can provide insights into the mechanisms underlying an observed phenomenon. Variable selection is especially important when the number of potential predictors is substantially larger than the sample size and sparsity can reasonably be assumed. In this webinar
I will provide an overview of Bayesian methods for variable selection, including spike-and-slab priors and shrinkage priors. I
</span><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:black;background-color:white">will focus on regression settings and will discuss prior constructions that account for
specific dependence structures in the covariates. I will also describe extensions of the models to non-Gaussian data. Throughout the talk, I will motivate the development of the models using specific applications from neuroimaging and genomics.</span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:1rem"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"><b> </b></span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"> </span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"><b>About the presenter:</b></span></p>
<p style="direction:ltr;margin-top:0px;margin-bottom:0px"><span style="font-family:"Aptos",sans-serif,serif,EmojiFont;font-size:12pt;color:black">Dr. Vannucci is Noah Harding Professor of Statistics. She is also an adjunct faculty member of the
UT M.D. Anderson Cancer Center, TX. Dr. Vannucci is generally interested in the development of Bayesian statistical models for complex problems and in applications to Science. She has contributed to methodological research on Bayesian variable selection techniques
for linear settings, mixture models and graphical models, and to related computational algorithms. Her research is often motivated by real problems that need to be addressed with suitable statistical methods. She has a solid history of scientific collaborations
and is particularly interested in applications of Bayesian inference to high-throughput genomics and to neuroscience and neuroimaging. Dr. Vannucci was the recipient of an NSF CAREER award in 2001. She is an elected Member of the International Statistical
Institute (ISI), and an elected Fellow of the American Statistical Association (ASA), the Institute of Mathematical Statistics (IMS), the American Association for the Advancement of Science (AAAS), and the International Society for Bayesian Analysis (ISBA).
She holds a Laurea (B.S.) in Mathematics and a Ph.D. in Statistics, both from the University of Florence, Italy.</span></p>
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<br>
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<div id="m_870947022011003356x_Signature">
<div><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:10pt;color:rgb(102,102,102)">Alexandra M. Schmidt</span></div>
<div><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:10pt;color:rgb(102,102,102)">Professor - The University Chair</span></div>
<div><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:10pt;color:rgb(102,102,102)">McGill University</span><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:12pt;color:rgb(0,0,0)"><br>
</span><span style="font-family:Aptos,Aptos_EmbeddedFont,Aptos_MSFontService,Calibri,Helvetica,sans-serif;font-size:10pt;color:rgb(0,0,0)"><b><a href="http://alex-schmidt.research.mcgill.ca/" id="m_870947022011003356OWAeb35a312-564b-4c98-0552-bf0bf99b806a" target="_blank">http://alex-schmidt.research.mcgill.ca/</a></b></span></div>
</div>
</div>
</div></div><br clear="all"><div><br></div><span class="gmail_signature_prefix">-- </span><br><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><i><span style="font-family:arial,sans-serif">Kelly C. M. Gonçalves</span></i></div><div><i><span style="font-family:arial,sans-serif">Professora Associada I</span></i></div><div><i><span style="font-family:arial,sans-serif">Departamento de Métodos Estatísticos</span></i></div><div><i><span style="font-family:arial,sans-serif">Universidade Federal do Rio de Janeiro</span></i><br></div><div><i><font face="arial, sans-serif"><a href="https://sites.google.com/dme.ufrj.br/kelly/" target="_blank">https://sites.google.com/dme.ufrj.br/kelly/</a></font><br></i></div></div></div></div>