Statistics, computing, functional programming, data science, Bayes, stochastic modelling, systems biology and bioinformatics
Breeze is the standard scientific and numerical library for Scala . For linear algebra operations, it builds on top of the Java library, netlib . This provides a nice interface to BLAS and related libraries which allows the use of native optimised libraries and will also gracefully fall back to using pure Java implementations if optimised native code libraries can’t be found. This is great, since…
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 either mean scaling up to distributed algorithms for very big data, or running algorithms for more moderately sized data sets very fast in parallel on GPUs. However, people unfamiliar with FP often…
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 go back to Part 1 . In the previous post we saw how to construct an MCMC algorithm utilising gradient information by considering a Langevin equation having our target distribution of interest as its…
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 this series, please go back to Part 1 . In the previous post we saw how to use Langevin dynamics to construct an approximate MCMC scheme using the gradient of the log target distribution. Each step of…
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, please go back to Part 1 . In the previous post we saw how the Metropolis algorithm could be used to generate a Markov chain targeting our posterior distribution. In high dimensions the diffusive…
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 back to Part 1 . In the previous post we derived the log posterior for the model and implemented it in a variety of programming languages and libraries. In this post we will construct a Markov chain…
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 Part 1 . In the previous post we looked at the basic modelling concepts, and how to fit the model using a variety of PPLs. In this post we will prepare for doing MCMC by considering the problem of…
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 posterior distribution can be sampled using a variety of different programming languages and libraries. Logistic regression Logistic regression is concerned with predicting a binary outcome based on…
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 (obituary). I completed the programme of talks by delivering the inaugural RSS North East Richard Boys lecture. For this, I decided that it would Continue reading MCMC code for Bayesian inference for a…
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 estimates due to lack of convergence to equilibrium (the burn-in problem), and shows how it is possible to modify MCMC algorithms Continue reading Unbiased MCMC with couplings