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<h1 id="toc_0">Further reading</h1>
<h3 id="toc_1">Quick links</h3>
<ul>
<li><a href="http://mc-stan.org">Stan website</a></li>
<li><a href="http://discourse.mc-stan.org">The Stan Forums</a> (get help from Stan developers and other users)</li>
<li><a href="http://mc-stan.org/users/documentation/index.html">Stan documentation</a> (links to various kinds of documentation for Stan)</li>
<li>Contributed talks and materials from the 2017 Stan conference (mostly about interesting applications of Stan), including slides & code (<a href="https://github.com/stan-dev/stancon_talks/blob/master/README.md">link to repository</a>)</li>
<li><a href="http://andrewgelman.com">Andrew Gelman's blog</a></li>
</ul>
<h3 id="toc_2">R packages from the Stan development team</h3>
<ul>
<li><a href="http://mc-stan.org/rstanarm">rstan</a> (R interface to Stan)</li>
<li><a href="http://mc-stan.org/rstanarm">rstanarm</a> (provides a traditional R formula interface for fitting common applied regression models with Stan, without having to write the Stan code yourself)</li>
<li><a href="http://mc-stan.org/bayesplot">bayesplot</a> (plotting)</li>
<li><a href="http://mc-stan.org/shinystan">shinystan</a> (interactive tables and visualizations)</li>
<li><a href="http://mc-stan.org/loo">loo</a> (efficient approximate leave-one-out cross-validation for Bayesian models)</li>
</ul>
<h3 id="toc_3">Hamiltonian Monte Carlo (HMC) and related background</h3>
<p>I highly recommend my Stan colleague Michael Betancourt's intro to HMC paper. Michael has a lot of very technical papers about HMC but this one is primarily focused on providing intuition (e.g., he has a whole section on the connection between HMC and the physics of planetary motion that I showed in a slide):</p>
<ul>
<li>A Conceptual Introduction to Hamiltonian Monte Carlo (<a href="https://arxiv.org/abs/1701.02434">paper</a>)</li>
</ul>
<p>This one is aimed at ecologists, but the HMC explanation is well written so it's a good read even if not an ecologist:</p>
<ul>
<li>Faster Estimation of Bayesian Models in Ecology using Hamiltonian Monte Carlo (<a href="http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12681/full">paper</a>)</li>
</ul>
<p>This case study from my colleague Bob Carpenter uses simple simulations to demonstrate how things get strange (and challenging) very quickly as the number of dimensions grows:</p>
<ul>
<li>Typical Sets and the Curse of Dimensionality (<a href="http://mc-stan.org/users/documentation/case-studies/curse-dims.html">case study</a>)</li>
</ul>
<h3 id="toc_4">Diagnostics, reparameterizations, priors</h3>
<ul>
<li>Diagnosing Biased Inference with Divergences (<a href="http://mc-stan.org/users/documentation/case-studies/divergences_and_bias.html">case study</a>)</li>
<li>How the Shape of a Weakly Informative Prior Affects Inferences (<a href="http://mc-stan.org/users/documentation/case-studies/weakly_informative_shapes.html">case study</a>)</li>
<li>The Impact of Reparameterization on Point Estimates (<a href="http://mc-stan.org/users/documentation/case-studies/mle-params.html">case study</a>)</li>
<li>The prior can generally only be understood in the context of the likelihood (<a href="https://arxiv.org/abs/1708.07487">paper</a>)</li>
<li>The QR Decomposition for Regression Models (<a href="http://mc-stan.org/users/documentation/case-studies/qr_regression.html">case study</a>)</li>
</ul>
<h3 id="toc_5">Visualization and graphical model checking</h3>
<ul>
<li>Visualization in Bayesian Workflow (<a href="https://arxiv.org/pdf/1709.01449.pdf">paper</a>)</li>
<li><strong>bayesplot</strong> package tutorials (<a href="http://mc-stan.org/bayesplot/articles/index.html">online vignettes</a>)</li>
</ul>
<h3 id="toc_6">Time series & spatial models</h3>
<ul>
<li><p>Chapter 10 in the <a href="https://github.com/stan-dev/stan/releases/download/v2.16.0/stan-reference-2.16.0.pdf">Stan Manual v2.16.0</a></p></li>
<li><p>Spatial Models in Stan: Intrinsic Auto-Regressive Models for Areal Data (<a href="http://mc-stan.org/users/documentation/case-studies/icar_stan.html">case study</a>)</p></li>
<li><p>Stan tutorial: <a href="http://tharte.github.io/mbt/">Modern Bayesian Tools for Time Series Analysis</a> contributed by Stan users Thomas P. Harte and R. Michael Weylandt.</p></li>
<li><p>You can also find tons of examples of simple and complicated time series modeling in Stan just by Googling </p></li>
</ul>
<h3 id="toc_7">Measurement error & missing data</h3>
<ul>
<li>Missing data: chapter 11 in the <a href="https://github.com/stan-dev/stan/releases/download/v2.16.0/stan-reference-2.16.0.pdf">Stan Manual v2.16.0</a></li>
<li>Measurement error: chapter 14 in the <a href="https://github.com/stan-dev/stan/releases/download/v2.16.0/stan-reference-2.16.0.pdf">Stan Manual v2.16.0</a></li>
</ul>
<h3 id="toc_8">Survival (duration) analysis</h3>
<p>Some Stan users have written Python and R libraries to help fit certain survival models using Stan: </p>
<ul>
<li><a href="https://github.com/hammerlab/survivalstan">Library of Stan Models for Survival Analysis</a> from Jacki Novik and HammerLab</li>
<li><p><a href="https://github.com/giabaio/survHE">survHE R package for fitting survival models via RStan</a> from Gianluca Baio</p></li>
<li><p>Chapters 11 through 15 in the <a href="https://github.com/stan-dev/stan/releases/download/v2.16.0/stan-reference-2.16.0.pdf">Stan Manual v2.16.0</a> all have content that relates in some way to survival models. </p></li>
<li><p>Paper and Stan code for survival analysis with shrinkage priors from Aki Vehtari (<a href="https://groups.google.com/forum/#!topic/stan-users/IOzu8_tkCSk">link</a>). (Note: this is a few years old so the Stan code may use some deprecated syntax) </p></li>
</ul>
<h3 id="toc_9">Model comparison, predictive performance, variable selection</h3>
<p>Note: some of these papers have been published in various journals but I'm including links to the free arXiv preprint versions.</p>
<ul>
<li>Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (<a href="https://arxiv.org/abs/1507.04544">arXiv</a>, <a href="https://github.com/stan-dev/loo">R package</a>)</li>
<li>Understanding predictive information criteria for Bayesian models (<a href="https://arxiv.org/abs/1307.5928">arXiv</a>)</li>
<li>Projection predictive variable selection using Stan+R (<a href="http://arxiv.org/abs/1508.02502">arXiv</a>, <a href="https://github.com/stan-dev/projpred">R package</a>)</li>
<li>Using stacking to average Bayesian predictive distributions (<a href="https://arxiv.org/abs/1704.02030">arXiv</a>)</li>
<li>Comparison of Bayesian predictive methods for model selection (<a href="https://arxiv.org/pdf/1503.08650.pdf">arXiv</a>)</li>
</ul>
<h3 id="toc_10">Item response theory</h3>
<p>Note: the Stan programs in these case studies were written using some old syntax that is now deprecated but still works (e.g., assignment with "<-"
instead of "=").</p>
<ul>
<li>Two-Parameter Logistic Item Response Model (<a href="http://mc-stan.org/users/documentation/case-studies/tutorial_twopl.html">case study</a>)</li>
<li>Rasch and Two-Parameter Logistic Item Response Models with Latent Regression (<a href="http://mc-stan.org/users/documentation/case-studies/rasch_and_2pl.html">case study</a>)</li>
<li>Partial Credit and Generalized Partial Credit Models with Latent Regression (<a href="http://mc-stan.org/users/documentation/case-studies/pcm_and_gpcm.html">case study</a>)</li>
<li>Rating Scale and Generalized Rating Scale Models with Latent Regression (<a href="http://mc-stan.org/users/documentation/case-studies/rsm_and_grsm.html">case study</a>)</li>
<li><p>Hierarchical Two-Parameter Logistic Item Response Model (<a href="http://mc-stan.org/users/documentation/case-studies/hierarchical_2pl.html">case study</a>)</p></li>
<li><p>Chapter 9, section 11 in the <a href="https://github.com/stan-dev/stan/releases/download/v2.16.0/stan-reference-2.16.0.pdf">Stan Manual v2.16.0</a></p></li>
<li><p>Fitting Bayesian item response models in Stata and Stan (<a href="https://arxiv.org/abs/1601.03443v2">arXiv</a>)</p></li>
</ul>
<h3 id="toc_11">Mixture models</h3>
<ul>
<li>Identifying Bayesian Mixture Models (<a href="http://mc-stan.org/users/documentation/case-studies/identifying_mixture_models.html">case study</a>)</li>
<li>Chapter 13 in the <a href="https://github.com/stan-dev/stan/releases/download/v2.16.0/stan-reference-2.16.0.pdf">Stan Manual v2.16.0</a></li>
</ul>
<h3 id="toc_12">Gaussian processes</h3>
<p>We didn't talk about Gaussian processes but I get asked about them a lot so here are some links just in case anyone is interested:</p>
<ul>
<li><p>Chapter 18 in the <a href="https://github.com/stan-dev/stan/releases/download/v2.16.0/stan-reference-2.16.0.pdf">Stan Manual v2.16.0</a></p></li>
<li><p>Hierarchical Gaussian Processes in Stan (<a href="https://github.com/stan-dev/stancon_talks/blob/master/README.md">Rob Trangucci's talk from StanCon 2017</a>)</p></li>
<li><p>Modeling the Rate of Public Mass Shootings with Gaussian Processes (<a href="https://github.com/stan-dev/stancon_talks/blob/master/README.md">Nathan Sanders' talk from StanCon 2017</a>)</p></li>
<li><p>GP example code recently updated by Rob Trangucci (<a href="https://github.com/stan-dev/example-models/tree/master/misc/gaussian-process">example models repository</a>)</p></li>
</ul>
<h3 id="toc_13">Economics-related textbooks</h3>
<p>This book is pretty good but it was written before Stan (everything in the book can be done in Stan though):</p>
<ul>
<li><a href="http://a.co/eDIIpUZ">An Introduction to Modern Bayesian Econometrics</a> by Tony Lancaster </li>
</ul>
<p>A forthcoming textbook that should be excellent but won't be published until 2018: </p>
<ul>
<li><a href="http://a.co/7J796MY">Bayesian Econometrics with Stan</a> by Jim Savage et al.</li>
</ul>
<p>The author of that forthcoming book, Jim Savage, has a blog that sometimes has good economics-related Stan content: </p>
<ul>
<li><a href="https://modernstatisticalworkflow.blogspot.com">Jim Savage blog</a></li>
</ul>
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