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schedule.html
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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang xml:lang>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" />
<meta name="author" content="Peter Ralph" />
<meta name="dcterms.date" content="2018-01-09" />
<title>Schedule :: Advanced Biological Statistics II: Bio 610, Winter 2018</title>
<style type="text/css">
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
span.underline{text-decoration: underline;}
div.column{display: inline-block; vertical-align: top; width: 50%;}
</style>
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<header>
<h1 class="title">Schedule :: Advanced Biological Statistics II: Bio 610, Winter 2018</h1>
<p class="author">Peter Ralph</p>
<p class="date">9 January 2018</p>
</header>
<h1 id="course-schedule">Course schedule</h1>
<p>The tentative schedule (subject to adjustment, especially towards the end) is (<strong>K</strong> referes to Kruschke):</p>
<dl>
<dt>Week 1 (<em>1/9</em>)</dt>
<dd><p><a href="slides/week_1.slides.html">(slides)</a> Recap of probability and likelihood; central limit theorem (<span class="math inline">\(\sqrt{n}\)</span>); Bayes’ rule. The beta-binomial distribution: putting a prior on the probability of success. <strong>(K ch. 4, 5, 6)</strong></p>
<ul>
<li><strong>Homework:</strong> (due 1/16) <a href="hws/week_1.html">MLE for beta-binomial</a> :: <a href="hws/week_1_soln.html">solution</a></li>
<li><strong>Demo:</strong> (from 1/11) <a href="demos/beta_binom.html">Beta-binomial analysis</a></li>
</ul>
</dd>
<dt>Week 2 (<em>1/16</em>)</dt>
<dd><p><a href="slides/week_2.slides.html">(slides)</a> Introduction to MCMC and Stan for sampling from posterior distributions, hierarchical models for binary responses, shrinkage. <strong>(K ch. 7, 9 and Intro to Stan)</strong></p>
<ul>
<li><strong>Homework:</strong> (due 1/26) <a href="hws/week_2.html">Error rates in ancient DNA</a> :: <a href="hws/week_2_soln.html">solution</a></li>
</ul>
</dd>
<dt>Week 3 (<em>1/23</em>)</dt>
<dd><p><a href="slides/week_3.slides.html">(slides)</a> Assessing power, model choice, and using simulation: looking more at shrinkage, posterior predictive sampling, model comparison. Logistic regression: robustly, including categorical factors. <strong>(K ch 13 and 21, with a bit of chapters 10-12)</strong></p>
</dd>
<dt>Week 4 (<em>1/30</em>)</dt>
<dd><p><a href="slides/week_4.slides.html">(slides)</a> Assessing power, model choice, and using simulation: looking more at shrinkage,</p>
</dd>
<dd><p>Count data: using Poisson regression and hierarchical modeling to fit overdispersion. Model selection by crossvalidation. <strong>(K ch 24)</strong></p>
<ul>
<li><strong>Homework:</strong> (due 2/9) <a href="hws/week_4.html">Coverage</a></li>
</ul>
</dd>
<dt>Week 5 (<em>2/6</em>)</dt>
<dd><p><a href="slides/week_5.slides.html">(slides)</a> Continuous (“metric”) data: groupwise means, univariate regression, robust regression by adjusting the noise distribution, friends of ANOVA. <strong>(K ch 16, 17, 18)</strong></p>
<ul>
<li><strong>Homework:</strong> (due 2/19) <a href="hws/week_6.html">Pipefish</a></li>
</ul>
</dd>
<dt>Week 6 (<em>2/13</em>)</dt>
<dd><p><a href="slides/week_6.slides.html">(slides)</a> Sparsifying priors and variable selection. An in-depth applied example, cumulative. <strong>(K ch 19, 20)</strong></p>
</dd>
<dt>Week 7 <em>(2/20)</em></dt>
<dd><p><a href="slides/week_7.slides.html">(slides)</a> Optimization and variational Bayes in Stan. Review of model building.</p>
<ul>
<li><strong>Homework:</strong> (due 2/27) <a href="hws/week_7.html">Diabetes</a></li>
</ul>
</dd>
<dt>Week 8 <em>(2/27)</em></dt>
<dd><p><a href="slides/week_8.slides.html">(slides)</a> Clustering and categorization: nonnegative matrix factorization. Also: <a href="slides/t-sne.slides.html">t-SNE in Stan</a>.</p>
</dd>
<dt>Week 9 <em>(3/6)</em></dt>
<dd><p><a href="slides/week_9.slides.html">(slides)</a> Time series: modeling local dependency, smoothing. Conditional independence.</p>
</dd>
<dt>Week 10 <em>(3/13)</em></dt>
<dd><p><a href="slides/week_10.slides.html">(slides)</a> Spatial and network covariance: sharing power between related locations. Priors to constrain visualization (e.g., regularized PCA).</p>
</dd>
</dl>
<p>And, finally: a <a href="slides/review.slides.html">review</a>.</p>
</body>
</html>