Blog Archives

Parallel Monte Carlo using Scala

February 23, 2014
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Parallel Monte Carlo using Scala

Introduction In previous posts I have discussed general issues regarding parallel MCMC and examined in detail parallel Monte Carlo on a multicore laptop. In those posts I used the C programming language in conjunction with the MPI parallel library in order to illustrate the concepts. In this post I want to take the example from […]

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Introduction to the particle Gibbs sampler

January 25, 2014
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Introduction to the particle Gibbs sampler

Introduction Particle MCMC (the use of approximate SMC proposals within exact MCMC algorithms) is arguably one of the most important developments in computational Bayesian inference of the 21st Century. The key concepts underlying these methods are described in a famously impenetrable “read paper” by Andrieu et al (2010). Probably the most generally useful method outlined […]

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Brief introduction to Scala and Breeze for statistical computing

December 30, 2013
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Brief introduction to Scala and Breeze for statistical computing

Introduction In the previous post I outlined why I think Scala is a good language for statistical computing and data science. In this post I want to give a quick taste of Scala and the Breeze numerical library to whet the appetite of the uninitiated. This post certainly won’t provide enough material to get started […]

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Scala as a platform for statistical computing and data science

December 23, 2013
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Scala as a platform for statistical computing and data science

There has been a lot of discussion on-line recently about languages for data analysis, statistical computing, and data science more generally. I don’t really want to go into the detail of why I believe that all of the common choices are fundamentally and unfixably flawed – language wars are so unseemly. Instead I want to […]

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A functional Gibbs sampler in Scala

October 4, 2013
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A functional Gibbs sampler in Scala

For many years I’ve had a passing interest in functional programming and languages which support functional programming approaches. I’m also quite interested in MOOCs and their future role in higher education. So I recently signed up for my first on-line course, Functional Programming Principles in Scala, via Coursera. I’m around half way through the course […]

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Marginal likelihood from tempered Bayesian posteriors

October 1, 2013
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Marginal likelihood from tempered Bayesian posteriors

Introduction In the previous post I showed that it is possible to couple parallel tempered MCMC chains in order to improve mixing. Such methods can be used when the target of interest is a Bayesian posterior distribution that is difficult to sample. There are (at least) a couple of obvious ways that one can temper […]

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Parallel tempering and Metropolis coupled MCMC

September 29, 2013
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Parallel tempering and Metropolis coupled MCMC

Introduction Parallel tempering is a method for getting Metropolis-Hastings based MCMC algorithms to work better on multi-modal distributions. Although the idea has been around for more than 20 years, and works well on many problems, it still isn’t routinely used in applications. I think this is partly because relatively few people understand how it works, […]

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Summary stats for ABC

September 1, 2013
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Summary stats for ABC

Introduction In the previous post I gave a very brief introduction to ABC, including a simple example for inferring the parameters of a Markov process given some time series observations. Towards the end of the post I observed that there were (at least!) two potential problems with scaling up the simple approach described, one relating […]

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Introduction to Approximate Bayesian Computation (ABC)

March 31, 2013
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Introduction to Approximate Bayesian Computation (ABC)

Many of the posts in this blog have been concerned with using MCMC based methods for Bayesian inference. These methods are typically “exact” in the sense that they have the exact posterior distribution of interest as their target equilibrium distribution, but are obviously “approximate”, in that for any finite amount of computing time, we can […]

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Getting started with Bayesian variable selection using JAGS and rjags

November 20, 2012
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Getting started with Bayesian variable selection using JAGS and rjags

Bayesian variable selection In a previous post I gave a quick introduction to using the rjags R package to access the JAGS Bayesian inference from within R. In this post I want to give a quick guide to using rjags for Bayesian variable selection. I intend to use this post as a starting point for […]

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