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  <div class="vevent h-event"> <h1 class="summary p-name">Galois Tech Talk: Empirical Sampling With Haskell</h1> <div class='date'><time class="dtstart dt-start" title="2011-05-10T10:30:00" datetime="2011-05-10T10:30:00">Tuesday, May 10, 2011 from 10:30</time>–<time class="dtend dt-end" title="2011-05-10T11:30:00" datetime="2011-05-10T11:30:00">11:30am</time></div> <div class="location vcard p-location h-card"> <a class="url" href='http://calagator.org/venues/202390439'><span class='fn org p-name'>Galois, Inc</span></a> <div class="adr p-adr h-adr"> <div class="street-address p-street-address">421 SW 6th Ave. Suite 300</div> <span class="locality p-locality">Portland</span> , <span class="region p-region">OR</span> <span class="postal-code p-postal-code">97204</span> <div class="country-name p-country-name">US</div> (<a href='https://maps.google.com/maps?q=421%20SW%206th%20Ave.%20Suite%20300,%20Portland%20OR%2097204%20US'>map</a>) </div> </div> <div class="description p-description"> <p>Presented by Chad Scherrer.</p> <p>Sampling from a large discrete distribution is a common problem in statistics. In this talk, we'll consider a real-world situation where the properties of the distribution cause common approaches to break down, and we'll arrive at a Haskell-based solution that fixes the problem.</p> </div> <h3>Links</h3> <ul> <li><a class="url u-url" href="http://corp.galois.com/blog/2011/5/4/tech-talk-empirical-sampling-with-haskell.html">Website</a></li> </ul> </div>