This book aims to provide a graduate-level introduction to advanced topics in Markov chain
Monte Carlo (MCMC) algorithms, as applied broadly in the Bayesian computational context …

Nested sampling parameter estimation differs from evidence estimation, in that it incurs an
additional source of uncertainty. This uncertainty affects estimates of parameter means and …

Large language models (LLMs), trained on diverse data effectively acquire a breadth of
information across various domains. However, their computational complexity, cost, and lack …

Several disciplines, such as econometrics, neuroscience, and computational psychology,
study the dynamic interactions between variables over time. A Bayesian nonparametric …

An effective approach for sampling from unnormalized densities is based on the idea of
gradually transporting samples from an easy prior to the complicated target distribution. Two …

Optimizing complex systems usually involves costly and time-consuming experiments,
where selecting the experiments to perform is fundamental. Bayesian optimization (BO) has …

We present microlux, which is a Jax-based code that can compute the binary microlensing
light curve and its derivatives both efficiently and accurately. The key feature of microlux is …

In a typical Bayesian inference problem, the data likelihood is not known. However, in recent
years, machine learning methods for density estimation can allow for inference using an …

We introduce synax, a novel library for automatically differentiable simulation of Galactic
synchrotron emission. Built on the JAX framework, synax leverages JAX's capabilities …

The Python Battery Optimisation and Parameterisation (PyBOP) package provides methods
for estimating and optimising battery model parameters, offering both deterministic and …