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(This article was first published on <strong><a href="http://blog.jumpingrivers.com/posts/2018/2018-01-23-stancon/"> R on The Jumping Rivers Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<li><a rel="nofollow" href="http://blog.jumpingrivers.com/posts/2018/2018-01-23-stancon/#highlights-from-stancon-2018" target="_blank">Highlights from StanCon 2018</a></li>
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<p><img src="https://i2.wp.com/blog.jumpingrivers.com/img/2018/stancon_comp.gif?w=400" data-recalc-dims="1"></p>
<p>This year we had the privelage of sponsoring <a rel="nofollow" href="http://mc-stan.org/events/stancon2018/" target="_blank">StanCon</a>. Unfortunately, we weren’t able to actually attend the conference. Rather than let our ticket go to waste, we ran a small <a rel="nofollow" href="http://blog.jumpingrivers.com/posts/2017/stancon_ticket_annoucement/" target="_blank">competition</a>, which <a rel="nofollow" href="http://home.ignacio.website/" target="_blank">Ignacio Martinez</a> won with his very cool (but in alpha stage) <a rel="nofollow" href="https://github.com/ignacio82/IMposterior" target="_blank">R package</a> – see gif above.</p>
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<h1>Highlights from StanCon 2018</h1>
<p>During my econ PhD I learned a lot about frequentist statistics. Alas, my training of Bayesian statistics was limited. Three years ago, I joined <a rel="nofollow" href="https://twitter.com/MathPolResearch" target="_blank">@MathPolResearch</a> and started delving into this whole new world. Two weeks ago, thanks to <a rel="nofollow" href="https://twitter.com/jumping_uk" target="_blank">@jumping_uk</a>, I was able to attend <a rel="nofollow" href="http://mc-stan.org/events/stancon2018/" target="_blank">StanCon</a>. This was an amazing experience, which allowed me to meet some great people and learn a lot from them. These are my highlights from the conference:</p>
<p>You’d better have a very good reason to not use hierarchical models. <a rel="nofollow" href="http://discourse.mc-stan.org/u/bgoodri/summary" target="_blank">Ben Goodrich</a>’s tutorial on advanced hierarchical models was great. Most social science data has a natural hierarchy and modeling it using Stan is easy! Slides for this three day tutorial are available here: [<a rel="nofollow" href="http://mc-stan.org/events/stancon2018/AHM/AHM1.pdf" target="_blank">day 1</a>, <a rel="nofollow" href="http://mc-stan.org/events/stancon2018/AHM/AHM2.pdf" target="_blank">day 2</a>, <a rel="nofollow" href="http://mc-stan.org/events/stancon2018/AHM/AHM3.pdf" target="_blank">day 3</a>].</p>
<p>Everyone should take his or her model to the <a rel="nofollow" href="https://cran.r-project.org/web/packages/loo/" target="_blank">loo</a>. <a rel="nofollow" href="https://twitter.com/avehtari" target="_blank">@avehtari</a>’s excellent tutorial covered cross-validation, reference predictive and projection predictive approaches for model assessment, selection and inference after model selection. This tutorial is <a rel="nofollow" href="https://github.com/avehtari/modelselection_tutorial" target="_blank">available online</a>, and everyone using Stan should do it.</p>
<p><a rel="nofollow" href="http://discourse.mc-stan.org/u/bob_carpenter/summary" target="_blank">Bob Carpenter</a>‘s tutorial on how to verify fit and diagnose convergence answered many practical and theoretical questions I had. Bob did a great job explaining how the effective sample sizes and potential scale reduction factors (’R hats’) are calculated. He also gives us some practical rules:</p>
<ul>
<li>We want R hat to be less than 1.05 and greater than 0.9</li>
<li>R hat equal to 1 does not guarantee convergence</li>
<li>An effective sample size between 50 and 100 is enough</li>
<li>Don’t be afraid to ask questions on the <a rel="nofollow" href="http://discourse.mc-stan.org/t/how-can-i-solve-bfmi-low-problem/3018/14" target="_blank">Stan forum</a></li>
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<p>The Bayesian Decision Making for Executives and Those who Communicate with Them series by Eric Novik and Jonathan Auerbach had some very good advice:</p>
<ul>
<li>Before model building, ask: What decisions are you trying to make? What is the cost of the wrong decision? What is the gain from a good decision?</li>
<li>During model building: Elicit enough information about the problem so that a generative model can be expressed. This is very hard. A lot depends on the industry (e.g., book publishers are very different from pharma companies).</li>
<li>After the model has been fit: Communicate the results so stakeholders can make a decision. Some things to keep in mind when doing so include:
<ul>
<li>Stakeholders should not care about p-values, Bayes factors or ROC curves (but sometimes do).</li>
<li>Stakeholders should care about the uncertainty in your estimates, but often they do not.</li>
<li>Stakeholders should know their loss or utility function, but they often do not.</li>
</ul>
</li>
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<p>To sum this up, the Stan developers are an incredibly talented and generous group of people that have created a useful and flexible programing language and a fantastic community around it. I look forward to future StanCons. A few other things that I am looking forward to in the nearer future (and I under<a rel="nofollow" href="https://www.youtube.com/watch?v=pWow8Qe1snQ" target="_blank"><em>Stan</em></a>d are coming soon…):</p>
<ul>
<li>A series of Coursera massive open online courses (MOOCs)</li>
<li>Support for parallel computing with MPI and GPUs</li>
<li>loo 2.0</li>
</ul>
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