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SR2 Chapter 2 Hard
1 March, 2020

Here’s my solution to the hard exercises in chapter 2 of McElreath’s Statistical Rethinking, 1st edition. When writing this up, I came across a very relevant article. We’ll solve these problems in two ways: using the counting method and using Bayes rule.

SR2 Chapter 2 Medium
29 February, 2020

Here’s my solutions to the medium exercises in chapter 2 of McElreath’s Statistical Rethinking, 1st edition. My intention is to move over to the 1nd edition when it comes out next month.

Speeding up Bayesian sampling with map_rect
9 August, 2019

Fitting a full Bayesian model can be slow, especially with a large dataset. For example, it’d be great to analyse the climate crisis questions in the European Social Survey (ESS), which typically has around 45,000 respondents from around Europe on a range of socio-political questions. There are two main ways of parallelising your Bayesian model in Stan: between-chain parallelisation and within-chain parallelisation. The first of these is very easy to implement (chains = 4, cores = 4) - it simply runs the algorithm once on each core and pools the posterior samples at the end. The second method is more complicated as it requires a non-trivial modification to the Stan model, but can bring with it large speedups if you have the cores available. In this post we’ll get a >5x speedup of ordinal regression using within-chain parallelisation.

Hierarchical Customer Lifetime Value
5 May, 2019

In a previous post, we described how a model of customer lifetime value (CLV) works, implemented it in Stan, and fit the model to simulated data. In this post, we’ll extend the model to use hierarchical priors in two different ways: centred and non-centred parameterisations. I’m not aware of any other HMC-based implementations of this hierarchical CLV model, so we’ll run some basic tests to check it’s doing the right thing. More specifically, we’ll fit it to a dataset drawn from the prior predictive distribution. The resulting fits pass the main diagnostic tests and the 90% posterior intervals capture about 91% of the true parameter values.

BDA3 Chapter 1 Exercise 9
13 April, 2019

Here’s my solution to exercise 9, chapter 1, of Gelman’s Bayesian Data Analysis (BDA), 3rd edition. There are solutions to some of the exercises on the book’s webpage.

…or you can find more in the archives.