var sms = '<div id="sms_bulletin">  <div id="sms_header">  <div class="do_not_print" id="arrow_left"><a href="?next=-25"><img src="//www2.math.ethz.ch/smsjs/images/arrow_left.png"/></a></div><div class="do_not_print" id="arrow_right"><a href="?next=-23"><img src="//www2.math.ethz.ch/smsjs/images/arrow_right.png"/></a></div>    <div class="sms_title">      <span style="float: left;">SMS</span>      <span>SWISS MATHEMATICAL SOCIETY</span>      <span style="float: right;">SMG</span>    </div>    <div class="sms_info">      <span style="float: left; width: 250px;">Bulletin no. 8</span>      <span style="float: right; text-align:right; width: 250px;">9.2 - 13.2.2026</span>      <span style="text-align: center; display: block; width: 250px; margin: auto; position: relative;">Winter Break 2025/26</span>    </div>  </div>  <div></div>  <div id="sms_content">    <div class="day">      <div class="day_title">        Monday, February 9      </div>      <div class="day_content">        <div class="day_content_item no_event">        No events scheduled today!        </div>      </div>    </div>    <div class="day">      <div class="day_title">        Tuesday, February 10      </div>      <div class="day_content">        <div class="day_content_item normal"><div class="event_location uni_la"><a class="external" href="https://www.epfl.ch">EPF Lausanne</a></div><div class="event_type"><a class="external" href="https://www.unil.ch/dsa/en/home/menuinst/seminaires--evenements/research-seminars.html">Talks in Actuarial Mathematics </a></div><br /><div class="event_speaker"> Martin Bladt (University of Copenhagen)</div><div class="event_title">Bayesian non-parametric survival estimation: stochastic hyperparameter sequences and distribution splicing <span class="abstract-button do_not_print">abstract</span></div><div class="abstract-text" style="display: none"><strong>Abstract:</strong><br /> A Bayesian non-parametric framework for studying time-to-event data is proposed, where the prior distribution is allowed to depend on an additional random source, and may update with the sample size. Such scenarios are natural, for instance, when considering empirical Bayes techniques or dynamic expert information. In this context, a natural stochastic class for studying the cumulative hazard function are conditionally inhomogeneous independent increment processes with non-decreasing sample paths, also known as mixed time-inhomogeneous subordinators or mixed non-decreasing additive processes. The asymptotic behaviour is studied by showing that Bayesian consistency and Bernstein--von~Mises theorems may be recovered under suitable conditions on the asymptotic negligibility of the stochastic prior sequences. The non-asymptotic behaviour of the posterior is also considered. Namely, upon conditioning, an efficient and exact simulation algorithm for the paths of the Beta Lévy process is provided. As a natural application, it is shown how the model can provide an appropriate definition of non-parametric spliced models. Spliced models target data where an accurate global description of both the body and tail of the distribution is desirable. The Bayesian non-parametric nature of the proposed estimators can offer conceptual and numerical alternatives to their parametric counterparts.</div><div class="event_time">11:00 &bull; EPF Lausanne, Extranef 109</div>        </div>      </div>    </div>    <div class="day">      <div class="day_title">        Wednesday, February 11      </div>      <div class="day_content">        <div class="day_content_item no_event">        No events scheduled today!        </div>      </div>    </div>    <div class="day">      <div class="day_title">        Thursday, February 12      </div>      <div class="day_content">        <div class="day_content_item normal"><div class="event_location uni_la"><a class="external" href="https://www.epfl.ch">EPF Lausanne</a></div><div class="event_type"><a class="external" href="https://theory.epfl.ch/seminar/">Theory and Combinatorics Seminar</a></div><br /><div class="event_speaker"> Kevin  Schewior  (U. Cologne)</div><div class="event_title">Recent Advances on Stochastic Boolean Function Evaluation <span class="abstract-button do_not_print">abstract</span></div><div class="abstract-text" style="display: none"><strong>Abstract:</strong><br />The Stochastic Boolean Function Evaluation (SBFE) problem is a fundamental problem in stochastic combinatorial optimization. A Boolean function is given (typically in compact representation), and the task is to find out the value of the function evaluated on independent Boolean random variables with known distributions and known inspection costs. In this talk, I will survey recent results on approximation algorithms for computing optimal adaptive and non-adaptive policies, as well as open questions, for this problem.</div><div class="event_time">11:00 &bull; EPF Lausanne</div>        </div>      </div>    </div>    <div class="day">      <div class="day_title">        Friday, February 13      </div>      <div class="day_content">        <div class="day_content_item no_event">        No events scheduled today!        </div>      </div>    </div>  </div>  <div id="sms_footer">    address: Alessandra Naldi Windler, Departement Mathematik, HG G 51.4, ETH Zurich, Raemistrasse 101, CH-8092 Zurich<br />    email: <a href="mailto:bulletin@math.ch">bulletin@math.ch</a> | phone: +41 44 632 3684 | url: <a href="https://math.ch">https://math.ch</a>    <br /><br /><img src="//www2.math.ethz.ch/smsjs/sms_logo.gif" width="150" alt="SMS/SMG Logo" title="SMS/SMG Logo" />  </div></div>';