This is the last video of a three part introduction to Bayesian data analysis aimed at *you* 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: Part 1 & Part 2.

This third video covers the *how?* of Bayesian data analysis: How to do it efficiently and how to do it in practice. But *covers* is really a big word, *briefly introduces* is really more appropriate. Along the way I will then *briefly introduce* Markov chain Monte Carlo, parameter spaces and the computational framework Stan:

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!

]]>This is video two of a three part introduction to Bayesian data analysis aimed at *you* who isn’t necessarily that well-versed in probability theory but that do know a little bit of programming. If you haven’t watched part one yet, I really recommend you do that first, here it is. This second video covers the *why?* of Bayesian data analysis: Why (and when) use it instead of some other method of analyzing data?

Be warned that this video barely scratches the surface of what Bayesian data analysis can be used for. The aim of the video is definitely not to be exhaustive but just to give you some intuition by showing you examples of why it’s useful. Finally, as the tutorial mentions a multitude of reasons for why Bayesian data analysis is useful, here is a multitude of fish by the master Hokusai.

]]>This is video one of a three part introduction to Bayesian data analysis aimed at *you* who isn’t necessarily that well-versed in probability theory but that do know a little bit of programming. I gave a version of this tutorial at the UseR 2015 conference, but I didn’t get around doing a screencast of it. Until now, that is! I should warn you that this tutorial is quite handwavey (but it’s also pretty short), and if you want a more rigorous video tutorial I can really recommend Richard McElreath’s YouTube lectures.

This first video covers the *what?* of Bayesian data analysis with part two and three covering the *why?* and the *how?*. I expect to be able to record part two and three over the next couple of weeks but, for now, here is part one:

The tutorial mentions an exercise which can be found here for R and here for python. Finally, as the tutorial is fish themed, here are some Bayescurious fish by the master Hokusai.

]]>Over the last two years I’ve occasionally been giving a very basic tutorial to Bayesian statistics using R and Stan. At the end of the tutorial I hand out an exercise for those that want to flex their newly acquired skills. I call this exercise *Bayesian computation with Stan and Farmer Jöns* and it’s pretty cool! Now, it’s not cool because of *me*, but because the expressiveness of Stan allowed me to write a small number of data analytic questions that quickly takes you from running a simple binomial model up to running a linear regression. Throughout the exercise you work with the same model code and each question just requires you to make a *minimal* change to this code, yet you will cover most models taught in a basic statistics course! Well, briefly at least… :) If you want to try out this exercise yourself, or use it for some other purpose, you can find it here:

Beginners Exercise: Bayesian computation with Stan and Farmer Jöns (R-markdown source)

Solutions to Bayesian computation with Stan and Farmer Jöns (R-markdown source)

My friend and colleague Christophe Carvenius also helped me translate this exercise into Python:

Python Beginners Exercise: Bayesian computation with Stan and Farmer Jöns

Python Solutions to Bayesian computation with Stan and Farmer Jöns

Now, this exercise would surely have been better if I’d used real data, but unfortunately I couldn’t find enough datasets related to cows… Finally, here is a depiction of farmer Jöns and his two lazy siblings by the great master Hokusai.

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