<p><div><div><div><div data-show-faces="false" data-height="21" data-width="100" data-layout="button_count" data-send="true" data-href="https://www.r-bloggers.com/short-course-on-bayesian-data-analysis-and-stan-23-25-aug-in-nyc/"></div></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="http://andrewgelman.com/2017/07/19/stan-short-course-nyc-23-25-aug/"> R – Statistical Modeling, Causal Inference, and Social Science</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Jonah “ShinyStan” Gabry, Mike “Riemannian NUTS” Betancourt, and I will be giving a <a rel="nofollow" href="https://www.eventbrite.com/e/learn-bayes-mcmc-and-stan-2017-with-andrew-gelman-jonah-gabry-michael-betancourt-tickets-36284546054" target="_blank">three-day short course next month</a> in New York, following the model of our successful courses in 2015 and 2016.</p>
<p>Before class everyone should install R, RStudio and RStan on their computers. (If you already have these, please update to the latest version of R and the latest version of Stan.) If problems occur please join the stan-users group and post any questions. It’s important that all participants get Stan running and bring their laptops to the course.</p>
<p>Class structure and example topics for the three days:</p>
<p>Day 1: Foundations<br>
Foundations of Bayesian inference<br>
Foundations of Bayesian computation with Markov chain Monte Carlo<br>
Intro to Stan with hands-on exercises<br>
Real-life Stan<br>
Bayesian workflow</p>
<p>Day 2: Linear and Generalized Linear Models<br>
Foundations of Bayesian regression<br>
Fitting GLMs in Stan (logistic regression, Poisson regression)<br>
Diagnosing model misfit using graphical posterior predictive checks<br>
Little data: How traditional statistical ideas remain relevant in a big data world<br>
Generalizing from sample to population (surveys, Xbox example, etc)</p>
<p>Day 3: Hierarchical Models<br>
Foundations of Bayesian hierarchical/multilevel models<br>
Accurately fitting hierarchical models in Stan<br>
Why we don’t (usually) have to worry about multiple comparisons<br>
Hierarchical modeling and prior information</p>
<p>Specific topics on Bayesian inference and computation include, but are not limited to:<br>
Bayesian inference and prediction<br>
Naive Bayes, supervised, and unsupervised classification<br>
Overview of Monte Carlo methods<br>
Convergence and effective sample size<br>
Hamiltonian Monte Carlo and the no-U-turn sampler<br>
Continuous and discrete-data regression models<br>
Mixture models<br>
Measurement-error and item-response models</p>
<p>Specific topics on Stan include, but are not limited to:<br>
Reproducible research<br>
Probabilistic programming<br>
Stan syntax and programming<br>
Optimization<br>
Warmup, adaptation, and convergence<br>
Identifiability and problematic posteriors<br>
Handling missing data<br>
Ragged and sparse data structures<br>
Gaussian processes</p>
<p>Again, <a rel="nofollow" href="https://www.eventbrite.com/e/learn-bayes-mcmc-and-stan-2017-with-andrew-gelman-jonah-gabry-michael-betancourt-tickets-36284546054" target="_blank">information on the course is here</a>.</p>
<p>The course is organized by <a rel="nofollow" href="https://www.landeranalytics.com/" target="_blank">Lander Analytics</a>.</p>
<p>The course is not cheap. Stan is open-source, and we organize these courses to raise money to support the programming required to keep Stan up to date. We hope and believe that the course is more than worth the money you pay for it, but we hope you’ll also feel good, knowing that this money is being used directly to support Stan R&amp;D.</p>
<p>The post <a rel="nofollow" href="http://andrewgelman.com/2017/07/19/stan-short-course-nyc-23-25-aug/" target="_blank">Short course on Bayesian data analysis and Stan 23-25 Aug in NYC!</a> appeared first on <a rel="nofollow" href="http://andrewgelman.com/" target="_blank">Statistical Modeling, Causal Inference, and Social Science</a>.</p>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://andrewgelman.com/2017/07/19/stan-short-course-nyc-23-25-aug/"> R – Statistical Modeling, Causal Inference, and Social Science</a></strong>.</div>
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