This is the 50th post to this blog. For my 25th post I provided a catalogue of my first 25 posts, and as promised then, I now provide a similar index for posts 25 to 50.
- 26. Multivariate data analysis (using R): a course and some lecture notes on MDA with R, but see post 43. for an updated version.
- 27. Gibbs sampling a Gaussian Markov random field (GMRF) using Java: for me this is of historic interest only, as I now use Scala for this sort of thing.
- 28. Metropolis Hastings MCMC when the proposal and target have differing support: Can I use an unbounded MH proposal for a distribution with bounded support?
- 29. MCMC on the Raspberry Pi: speed-testing the original Raspberry Pi. Note that the new Raspberry Pi 2 is around 6 times faster than the original Pi, which makes it even more interesting…
- 30. Inlining JAGS models in R scripts for rjags: an easy way to use the JAGS MCMC engine from within R
- 31. Keeping R up to date on Ubuntu linux: How to get and maintain the latest version of R on Ubuntu
- 32. Getting started with Bayesian variable selection using JAGS and rjags: Introducing BVS using JAGS
- 33. Introduction to Approximate Bayesian Computation (ABC): Simple ABC for an intractable Markov process
- 34. Summary stats for ABC: continuing the previous post
- 35. Parallel tempering and Metropolis coupled MCMC: Tempering and MCMCMC
- 36. Marginal likelihood from tempered Bayesian posteriors: Continuation of the previous post, thinking about normalising constants
- 37. A functional Gibbs sampler in Scala: how to write MCMC algorithms in Scala without mutable variables
- 38. Scala as a platform for statistical computing and data science: post explaining why I think Scala is a great language for doing statistical computing
- 39. Brief introduction to Scala and Breeze for statistical computing: a quick tutorial introduction
- 40. Introduction to the particle Gibbs sampler: explanation of how and why “particle Gibbs” works
- 41. Parallel Monte Carlo using Scala: post showing how trivial it is to effectively parallelise functional codes for effective exploitation of multiple cores
- 42. Tuning particle MCMC algorithms: post discussing how to choose an appropriate number of particles to use in PMMH
- 43. Statistics for Big Data: a graduate course on statistics for big data, with some associated lecture notes (an update of the MDA notes from post 26.)
- 44. Statistical computing languages at the RSS: Some lecture slides on Scala for statistical computing, and a small github repo containing easy-to-run code examples
- 45. One-way ANOVA with fixed and random effects from a Bayesian perspective: how to think about the difference between “fixed” and “random” effects when you’re a Bayesian
- 46. Calling Scala code from R using jvmr: extending R with Scala
- 47. Inlining Scala Breeze code in R using jvmr and sbt: inlining Breeze code in R
- 48. Calling R from Scala sbt projects: embedding R in Scala
- 49. Scala for Machine Learning [book review]: a disappointing read
- 50. Index to first 50 posts [this post]
If I make it to post 75, I’ll do the same again.
Darren Wilkinson's blog 
Hi Darren,
Nice set of posts, thanks.
I’m curious – I noticed that the content from some of these made it into (the second edition of?) your book, which I really like. Did the blog posts come first and then get added to the book or vice-versa? I.e. was drafting it up in blog post form helpful for converting into a book later?
Cheers.
Both! Mainly the blog posts came first. I wrote the first edition before starting my blog, and used the blog to develop and update some of the topics covered in the first edition. I then included some of those topics in the second edition. For example, the explanation of PMMH in my book was cut-and-pasted from the blog and then edited. In general I do think it is useful, not least because it gives an opportunity for feedback. But there are also a couple of posts where I’ve wanted to explain something from the second edition of my book on the blog, so it works both ways. Cheers,