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<p>This is the last video of a three part introduction to Bayesian data analysis aimed at <em>you</em> who isn’t necessarily that well-versed in probability theory but that do know a little bit of programming. If you haven’t watched the other parts yet, I really recommend you do that first: <a target="_blank" rel="nofollow" href="http://www.sumsar.net/blog/2017/02/introduction-to-bayesian-data-analysis-part-one/">Part 1</a> &amp; <a target="_blank" rel="nofollow" href="http://www.sumsar.net/blog/2017/02/introduction-to-bayesian-data-analysis-part-two/">Part 2</a>. </p>
<p>This third video covers the <em>how?</em> of Bayesian data analysis: How to do it efficiently and how to do it in practice. But <em>covers</em> is really a big word, <em>briefly introduces</em> is really more appropriate. Along the way I will then <em>briefly introduce</em> Markov chain Monte Carlo, parameter spaces and the computational framework <a target="_blank" rel="nofollow" href="http://mc-stan.org/">Stan</a>:</p>
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<p>After having watched this video tutorial (and having done the exercises) you won’t be an expert in any way, like the expert fisherman depicted below by the master Hokusai. But, at least, you will have caught your own first fish!</p>
<p><img alt="" src="https://i1.wp.com/www.sumsar.net/images/posts/2017-05-08-introduction-to-bayesian-data-analysis-part-three/fishing_man.jpeg?w=456" data-recalc-dims="1"></p>
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