# Welcome

Have a gander at a recent post...

##### SR2 Chapter 3 Medium

###### 5 April, 2020

Here’s my solution to the medium exercises in chapter 3 of McElreath’s Statistical Rethinking, 2nd edition.

##### SR2 Chapter 3 Hard

###### 5 April, 2020

Here’s my solutions to the hard exercises in chapter 3 of McElreath’s Statistical Rethinking, 2nd edition.

##### 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.

…or you can find more in the archives.