<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/stancon-is-next-week-jan-10-12-2018/"></div></div><div></div></div><div></div></div>
<div>
(This article was first published on <strong><a href="http://andrewgelman.com/2018/01/04/stancon-next-week-jan-10-12-2018/"> 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>It looks <a rel="nofollow" href="http://mc-stan.org/events/stancon2018/" target="_blank">pretty cool</a>!</p>
<p><strong>Wednesday, Jan 10</strong></p>
<p>Invited Talk: Predictive information criteria in hierarchical Bayesian models for clustered data. Sophia Rabe-Hesketh and Daniel Furr (U California, Berkely) 10:40-11:30am</p>
<p>Does the New York City Police Department rely on quotas? Jonathan Auerbach (Columbia U) 11:30-11:50am</p>
<p>Bayesian estimation of mechanical elastic constants. Ben Bales, Brent Goodlet, Tresa Pollock, Linda Petzold (UC Santa Barbara) 11:50am-12:10pm</p>
<p>Joint longitudinal and time-to-event models via Stan. Sam Brilleman, Michael Crowther, Margarita Moreno-Betancur, Jacqueline Buros Novik, Rory Wolfe (Monash U, Columbia U) 12:10-12:30pm<br>
Lunch 12:30-2:00pm</p>
<p>ScalaStan. Joe Wingbermuehle (Cibo Technologies) 2:00-2:20pm<br>
A tutorial on Hidden Markov Models using Stan. Luis Damiano, Brian Peterson, Michael Weylandt 2:20-2:40pm</p>
<p>Student Ornstein-Uhlenbeck models served three ways (with applications for population dynamics data). Aaron Goodman (Stanford U) 2:40-3:00pm</p>
<p>SlicStan: a blockless Stan-like language. Maria I. Gorinova, Andrew D. Gordon, Charles Sutton (U of Edinburgh) 3:00-3:20pm<br>
Break 3:20-4:00pm</p>
<p>Invited Talk: Talia Weiss (MIT) 4:00-4:50pm</p>
<p><strong>Thursday, Jan 11</strong></p>
<p>Invited Talk: Sean Taylor and Ben Letham (Facebook) 10:40-11:30am</p>
<p>NPCompare: a package for nonparametric density estimation and two populations comparison built on top of PyStan. Marco Inacio (U of São Paulo/UFSCar) 11:30-11:50am</p>
<p>Introducing idealstan, an R package for ideal point modeling with Stan. Robert Kubinec (U of Virginia) 11:50am-12:10pm</p>
<p>A brief history of Stan. Daniel Lee (Generable) 12:10-12:30pm<br>
Lunch 12:30-1:30pm</p>
<p>Computing steady states with Stan’s nonlinear algebraic solver. Charles C. Margossian (Metrum, Columbia U) 1:30-1:50pm</p>
<p>Flexible modeling of Alzheimer’s disease progression with I-Splines. Arya A. Pourzanjani, Benjamin B. Bales, Linda R. Petzold, Michael Harrington (UC Santa Barbara) 1:50-2:10pm</p>
<p>Intrinsic Auto-Regressive (ICAR) Models for Spatial Data, Mitzi Morris (Columbia U) 2:10-2:30pm</p>
<p>Modeling/Data Session + Classes 2:30-4:10pm</p>
<p>Open session for consultations on modeling and data problems with Stan developers and modelers. 2:30-4:10pm</p>
<p>Session 3 of Intro to Stan 2:30-4:10pm</p>
<p>2:30-3:30pm Have I converged successfully? How to verify fit and diagnose fit problems, Bob Carpenter</p>
<p>What is new to Stan 3:30-4:10pm</p>
<p>Invited Talk: Manuel Rivas (Stanford U) 4:00-4:50pm</p>
<p><strong>Friday, Jan 12</strong></p>
<p>Invited Talk: Susan Holmes (Stanford U) 10:40-11:30am</p>
<p>Aggregate random coefficients logit — a generative approach. Jim Savage, Shoshana Vasserman 11:30-11:50am</p>
<p>The threshold test: Testing for racial bias in vehicle searches by police. Camelia Simoiu, Sam Corbett-Davies, Sharad Goel, Emma Pierson (Stanford U) 11:50am-12:10pm</p>
<p>Assessing the safety of Rosiglitazone for the treatment of type II diabetes. Konstantinos Vamvourellis, K. Kalogeropoulos, L. Phillips (London School of Economics and Political Science) 12:10-12:30pm<br>
Lunch 12:30-1:30pm</p>
<p>Causal inference with the g-formula in Stan. Leah Comment (Harvard U) 1:30-1:50pm<br>
Bayesian estimation of ETAS models with Rstan. Fausto Fabian Crespo Fernandez (Universidad San Francisco de Quito) 1:50-2:10pm</p>
<p>Invited Talk: Andrew Gelman 2:10-3:00 (Columbia U) (virtual)</p>
<p><strong>Classes/Tutorials</strong></p>
<p>We have tutorials that start at the crack of 8am for those desiring further edification beyond the program—these do not run in parallel to the main session but do run parallel to each other:</p>
<p>Introduction to Stan: Know how to program? Know basic statistics? Curious about Bayesian analysis and Stan? This is the course for you. Hands on, focused and an excellent way to get started working in Stan. Two hours every day, 6 hours total. Jonah Sol Gabry.</p>
<p>Executive decision making the Bayesian way: This is for non-technical managers and technical folks who need to communicate with managers to learn the core of decision making under uncertainty. One hour every day. Jonathan Auerbach, Breck Baldwin, Eric Novik.</p>
<p>Advanced Hierarchical Models in Stan: The hard stuff. Very interactive, very intense. Topics vary by day. Ben Goodrich.</p>
<p>Model assessment, model selection and inference after model selection. Aki Vehtari.</p>
<p>How to develop for Stan at the C++ level: Overview of Stan C++ architecture and build/development process for contributors. Charles Christopher Margossian.</p>
<p>[edited by Aki: added Model selection tutorial]</p>
<p>The post <a rel="nofollow" href="http://andrewgelman.com/2018/01/04/stancon-next-week-jan-10-12-2018/" target="_blank">StanCon is next week, Jan 10-12, 2018</a> appeared first on <a rel="nofollow" href="http://andrewgelman.com/" target="_blank">Statistical Modeling, Causal Inference, and Social Science</a>.</p>
	<p>
<div>
<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/2018/01/04/stancon-next-week-jan-10-12-2018/"> R – Statistical Modeling, Causal Inference, and Social Science</a></strong>.</div>
<hr>
<a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers.com</a> offers <strong><a rel="nofollow" href="https://feedburner.google.com/fb/a/mailverify?uri=RBloggers">daily e-mail updates</a></strong> about <a rel="nofollow" href="https://www.r-project.org/" title="The R Project for Statistical Computing">R</a> news and <a rel="nofollow" href="https://www.r-bloggers.com/search/tutorial" title="R tutorials">tutorials</a> on topics such as: <a rel="nofollow" href="https://www.r-bloggers.com/search/data%20science" title="Data science">Data science</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Big%20Data" title="Big Data">Big Data, <a rel="nofollow" href="https://www.r-users.com/" title="R jobs">R jobs</a>, visualization (<a rel="nofollow" href="https://www.r-bloggers.com/search/ggplot2" title="ggplot and ggplot2 tutorials">ggplot2</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/boxplot" title="Boxplots using lattice and ggplot2 tutorials">Boxplots</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/map" title="Maps and gis">maps</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/animation" title="Animation in R">animation</a>), programming (<a rel="nofollow" href="https://www.r-bloggers.com/search/RStudio" title="RStudio IDE for R">RStudio</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/sweave" title="Sweave and literate programming">Sweave</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/LaTeX" title="LaTeX in R">LaTeX</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/SQL" title="SQL and databases">SQL</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/eclipse" title="Eclipse IDE for R">Eclipse</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/git" title="git and github, Version Control System">git</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/hadoop" title="Large data in R using Hadoop">hadoop</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Web+Scraping" title="Web Scraping of google, facebook, yahoo, twitter and more using R">Web Scraping</a>) statistics (<a rel="nofollow" href="https://www.r-bloggers.com/search/regression" title="Regressions and ANOVA analysis tutorials">regression</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/PCA" title="principal component analysis tutorial">PCA</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/time+series" title="Time series">time series</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/trading" title="finance trading">trading</a>) and more...
<img width="1" alt="" src="http://feeds.feedburner.com/~r/RBloggers/~4/V6ULqECL8fs" height="1"></a></div>