Sequential Neural Posterior and Likelihood Approximation

A paper on simulation-based inference where we present an algorithm that learns both the posterior and the likelihood of the model.

Efficient inference for stochastic differential mixed-effects models using correlated particles pseudo-marginal algorithms

In this paper, we use correlated particle pseudo-marginal Metropolis-Hastings (CPMMH) for accelerating inference for stochastic differential equation mixed-effects models (SDEMEMs).

Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation

A deep learning paper where we introduce the novel architecture partially exchangeable networks (PEN). In the paper, we use PEN for learning summary statistics for ABC.

Accelerating delayed-acceptance Markov chain Monte Carlo algorithms

An MCMC paper where we develop an accelerated version of the delayed-acceptance algorithm. As a case study, we consider the computational challenging and highly complex problem of modeling protein folding.

Implementation of some ABC algorithms in Julia

Generic implementations of some ABC algorithms in Julia.

Introduction to likelihood-free inference

An introductory Jupyter notebook tutorial on likelihood-free inference.

Pioneers-pp: Bayesian statistics and ABC talk

Slides, examples, and computer exercises for my presentation at the Pioneers of Probabilistic Programming Meet-up group.

Bayes@Lund 2019

Slides for my talk at Bayes@Lund 2019.

Useful terminal/git commands

A list of some useful terminal/git commands that I regularly use.