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Scala-view: Animate streams of images
Introduction In the previous post I discussed how comonads can be useful for structuring certain kinds of scientific and statistical computations. Two of the examples I gave were concerned with the...
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Using EvilPlot with scala-view
EvilPlot EvilPlot is a new functional data visualisation library for Scala. Although there are several data viz libraries for Scala, this new library has a nice functional API for producing attractive,...
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Monadic probabilistic programming in Scala with Rainier
Introduction Rainier is an interesting new probabilistic programming library for Scala recently open-sourced by Stripe. Probabilistic programming languages provide a computational framework for...
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Bayesian hierarchical modelling with Rainier
Introduction In the previous post I gave a brief introduction to Rainier, a new HMC-based probabilistic programming library/DSL for Scala. In that post I assumed that people were using the latest...
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Stochastic Modelling for Systems Biology, third edition
The third edition of my textbook, Stochastic Modelling for Systems Biology has recently been published by Chapman & Hall/CRC Press. The book has ISBN-10 113854928-2 and ISBN-13 978-113854928-9. It...
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The smfsb R package
Introduction In the previous post I gave a brief introduction to the third edition of my textbook, Stochastic modelling for systems biology. The algorithms described in the book are illustrated by...
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The scala-smfsb library
In the previous post I gave a very quick introduction to the smfsb R package. As mentioned in that post, although good for teaching and learning, R isn’t a great language for serious scientific...
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Stochastic reaction-diffusion modelling
Introduction There is a fairly large literature on reaction-diffusion modelling using partial differential equations (PDEs). There is also a fairly large literature on stochastic modelling of coupled...
View ArticleWrite your own general-purpose monadic probabilistic programming language...
Background In May I attended a great workshop on advances and challenges in machine learning languages at the CMS in Cambridge. There was an a good mix of people from different disciplines, and a bit...
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A probability monad for the bootstrap particle filter
Introduction In the previous post I showed how to write your own general-purpose monadic probabilistic programming language from scratch in 50 lines of (Scala) code. That post is a pre-requisite for...
View ArticleIndex to first 75 posts
This is the 75th post to this blog. Every 25 posts I produce an index of posts so far for easy reference. If I make it to post 100 I’ll do something similar. 25. Catalogue of my first 25 blog posts 50....
View ArticleUnbiased MCMC with couplings
Yesterday there was an RSS Read Paper meeting for the paper Unbiased Markov chain Monte Carlo with couplings by Pierre Jacob, John O’Leary and Yves F. Atchadé. The paper addresses the bias in MCMC...
View ArticleMCMC code for Bayesian inference for a discretely observed stochastic kinetic...
In June this year the (twice COVID-delayed) Richard J Boys Memorial Workshop finally took place, celebrating the life and work of my former colleague and collaborator, who died suddenly in 2019...
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Bayesian inference for a logistic regression model (Part 1)
Part 1: The basics Introduction This is the first in a series of posts on MCMC-based fully Bayesian inference for a logistic regression model. In this series we will look at the model, and see how the...
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Bayesian inference for a logistic regression model (Part 2)
Part 2: The log posterior Introduction This is the second part in a series of posts on MCMC-based Bayesian inference for a logistic regression model. If you are new to this series, please go back to...
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Bayesian inference for a logistic regression model (Part 3)
Part 3: The Metropolis algorithm Introduction This is the third part in a series of posts on MCMC-based Bayesian inference for a logistic regression model. If you are new to this series, please go...
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Bayesian inference for a logistic regression model (Part 4)
Part 4: Gradients and the Langevin algorithm Introduction This is the fourth part in a series of posts on MCMC-based Bayesian inference for a logistic regression model. If you are new to this series,...
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Bayesian inference for a logistic regression model (Part 5)
Part 5: the Metropolis-adjusted Langevin algorithm (MALA) Introduction This is the fifth part in a series of posts on MCMC-based Bayesian inference for a logistic regression model. If you are new to...
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Bayesian inference for a logistic regression model (Part 6)
Part 6: Hamiltonian Monte Carlo (HMC) Introduction This is the sixth part in a series of posts on MCMC-based Bayesian inference for a logistic regression model. If you are new to this series, please...
View ArticleAn introduction to functional programming for scalable statistical computing...
Functional programming (FP) languages are great for statistical computing, computational statistics, and machine learning. They are particularly well-suited to scalable computation, where this could...
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