<div dir="ltr">Caros redistas,<div><br></div><div>Nesta quinta-feira, a Escola de Matemática Aplicada da FGV receberá o Dr. Jere Koskela, da Universidade de Warwick (Reino Unido) para falar sobre MCMC no contexto de um problema envolvendo árvores filogenéticas. O seminário ocorre às 16h, e o link encontra-se abaixo.</div><div><br></div><div>Esperamos vocês!</div><div><br></div><div>Cordialmente,</div><div><br></div><div>Luiz</div><div><br></div><div><br><div class="gmail_quote"><div lang="PT-BR" link="#0563C1" vlink="#954F72"><div class="m_2185485564414613869WordSection1"><br>
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<b><span style="font-size:12.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#2f5597">GRADIENT-BASED, NONREVERSIBLE MCMC FOR COALESCENT TREES</span></b><b><span style="font-size:12.5pt;line-height:115%;font-family:"Arial",sans-serif"><u></u><u></u></span></b></p>
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<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#595959">Posterior distributions arising out of coalescent-based models of genetic diversity are nearly always intractable. Markov chain Monte Carlo methods, typically based
 on the Metropolis-Hastings algorithm, are gold-standard tools for sampling from these posteriors. However, their computational cost scales notoriously badly with problem complexity, so that their practical use is restricted to data sets which are small by
 modern standards. Over the past 5 years, several classes of nonreversible MCMC methods which use the gradient of the posterior density to guide chains during runs have become increasingly prominent as competitors of Metropolis-Hastings. These methods have
 better theoretical scaling properties than Metropolis-Hastings, but cannot be readily implemented for coalescent models because a posterior defined on discrete tree topologies does not have a natural analogue of a gradient. I will demonstrate how embedding
 spaces of coalescent trees into a continuous space facilitates the use of gradient information in algorithm specification, and also provides a framework for designing adaptive MCMC algorithms in a principled way. I will also outline how the same approach can
 be generalised to other state spaces involving boundaries, discrete variables, or disconnected regions. Comparisons with Metropolis-Hastings on simple coalescent examples show that these methods speed up mixing over the posterior, sometimes dramatically.<u></u><u></u></span></p>
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^{<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#595959">Texto informado pelo autor</span>}^{<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#3b3838">.
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<b><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#1f497d">Quando e Onde:</span></b><u></u><u></u></p>
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<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#595959">09 de Setembro de 2021, às
<b>16h</b></span><span style="color:#595959"><u></u><u></u></span></p>
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<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#595959">Via Zoom</span><span style="color:#595959"><u></u><u></u></span></p>
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<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#595959">Registre-se com antecedência para este seminário:
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<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#595959">Após o registro, você receberá um e-mail de confirmação contendo informações sobre conexão no seminário.</span><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif"><u></u><u></u></span></p>
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<b><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#1f4e79">Palestrante:</span></b><b><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif"><u></u><u></u></span></b></p>
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<b><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#595959">Jere Koskela</span></b><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#595959">
</span><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#595959">-
</span><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#595959">holds an MMORSE, a MSc and a PhD from the University of Warwick (UK), where he is currently an Associate Professor at the Department of Statistics. He was also
 a postdoctoral researcher  at  Institut für Mathematik, Technische Universität Berlin, Germany. His research interests include Monte Carlo methods, statistical inference from stochastic processes and in settings with intractable likelihood, Bayesian nonparametric
 statistics, coalescent processes, and mathematical population genetics.<u></u><u></u></span></p>
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<a href="https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fportal.fgv.br%2Fredes-sociais&data=04%7C01%7Cemap%40fgv.br%7Cfb2f4c252d934ceca32408d96e1487dd%7C79f6b639ab1242808077bdbeef869b33%7C0%7C0%7C637661856725682181%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=dd8h%2BNS%2BE2xii1OJCyVoG%2FKTSIE1dhWaFAW5Tk5coZI%3D&reserved=0" target="_blank"><span style="font-size:13.5pt;font-family:"Times New Roman",serif;color:blue;text-decoration:none"><img border="0" width="50" height="53" style="width:.525in;height:.55in" id="m_2185485564414613869_x0000_i1163" src="cid:17bc11c075020fd909c" alt="Mais"></span></a><u></u><u></u></p>
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<span style="font-size:10.0pt;color:#1f497d"><u></u> </span></p></td><td width="0%" style="width:.04%;padding:0cm 0cm 0cm 0cm;height:27.0pt"></td></tr></tbody></table></div></div></div><div><br></div>-- <br><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div dir="ltr" style="color:rgb(80,0,80)"><div dir="ltr">Luiz Max Fagundes de Carvalho</div><div dir="ltr">Lecturer, School of Applied Mathematics (EMAp), Getúlio Vargas Foundation (FGV), Brazil.</div><div dir="ltr">Praia de Botafogo, 190, Sala 511, Rio de Janeiro, RJ 22250-900.</div><div dir="ltr">Brazil</div></div></div></div></div></div></div></div>