Blog Archives

Index to first 50 posts

April 10, 2015
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Index to first 50 posts

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. 25. Catalogue of my first 25 blog posts 26. Multivariate data analysis (using R): a course and some lecture … Continue reading Index to first 50 posts

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Scala for Machine Learning [book review]

April 9, 2015
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Scala for Machine Learning [book review]

Nicolas, Patrick R. (2014) Scala for Machine Learning, Packt Publishing: Birmingham, UK. Full disclosure: I received a free electronic version of this book from the publisher for the purposes of review. There is clearly a market for a good book about using Scala for statistical computing, machine learning and data science. So when the publisher … Continue reading Scala for Machine Learning [book review]

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Calling R from Scala sbt projects

January 24, 2015
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Calling R from Scala sbt projects

Overview In previous posts I’ve shown how the jvmr CRAN R package can be used to call Scala sbt projects from R and inline Scala Breeze code in R. In this post I will show how to call to R from a Scala sbt project. This requires that R and the jvmr CRAN R package … Continue reading Calling R from Scala sbt projects

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Inlining Scala Breeze code in R using jvmr and sbt

January 3, 2015
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Inlining Scala Breeze code in R using jvmr and sbt

Introduction In the previous post I showed how to call Scala code from R using sbt and jvmr. The approach described in that post is the one I would recommend for any non-trivial piece of Scala code – mixing up code from different languages in the same source code file is not a good strategy … Continue reading Inlining Scala Breeze code in R using jvmr and sbt

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Calling Scala code from R using jvmr

January 2, 2015
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Calling Scala code from R using jvmr

Introduction In previous posts I have explained why I think that Scala is a good language to use for statistical computing and data science. Despite this, R is very convenient for simple exploratory data analysis and visualisation – currently more convenient than Scala. I explained in my recent talk at the RSS what (relatively straightforward) … Continue reading Calling Scala code from R using jvmr

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One-way ANOVA with fixed and random effects from a Bayesian perspective

December 22, 2014
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One-way ANOVA with fixed and random effects from a Bayesian perspective

This blog post is derived from a computer practical session that I ran as part of my new course on Statistics for Big Data, previously discussed. This course covered a lot of material very quickly. In particular, I deferred introducing notions of hierarchical modelling until the Bayesian part of the course, where I feel it … Continue reading One-way ANOVA with fixed and random effects from a Bayesian perspective

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Statistical computing languages at the RSS

November 22, 2014
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Statistical computing languages at the RSS

On Friday the Royal Statistical Society hosted a meeting on Statistical computing languages, organised by my colleague Colin Gillespie. Four languages were presented at the meeting: Python, Scala, Matlab and Julia. I presented the talk on Scala. The slides I presented are available, in addition to the code examples and instructions on how to run … Continue reading Statistical computing languages at the RSS

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Statistics for Big Data

November 22, 2014
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Statistics for Big Data

Doctoral programme in cloud computing for big data I’ve spent much of this year working to establish our new EPSRC Centre for Doctoral Training in Cloud Computing for Big Data, which partly explains the lack of posts on this blog in recent months. The CDT is now established, with 11 students in the first cohort, … Continue reading Statistics for Big Data

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Tuning particle MCMC algorithms

June 8, 2014
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Tuning particle MCMC algorithms

Several papers have appeared recently discussing the issue of how to tune the number of particles used in the particle filter within a particle MCMC algorithm such as particle marginal Metropolis Hastings (PMMH). Three such papers are: Doucet, Arnaud, Michael Pitt, and Robert Kohn. Efficient implementation of Markov chain Monte Carlo when using an unbiased […]

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Tuning particle MCMC algorithms

June 8, 2014
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Tuning particle MCMC algorithms

Several papers have appeared recently discussing the issue of how to tune the number of particles used in the particle filter within a particle MCMC algorithm such as particle marginal Metropolis Hastings (PMMH). Three such papers are: Doucet, Arnaud, Michael Pitt, and Robert Kohn. Efficient implementation of Markov chain Monte Carlo when using an unbiased … Continue reading Tuning particle MCMC algorithms

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