Unbiased 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...
View ArticleUsing optimised BLAS and LAPACK libraries with Scala Breeze
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...
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