Joeri Hermans


I'm a PhD Student at on quest to mechanize the scientific method by building artificial intelligence systems. This involves working on statistics, likelihood-free inference, machine learning and high performance computing with the goal to effectively apply these to domain sciences such as physics and astronomy. Although I'm not a physicist by training, I'm working towards combining these fields in the future.

Latest Work

Likelihood-free Markov chain Monte Carlo using Approximate Likelihood Ratios

We propose a novel approach for posterior sampling with intractable likelihoods. This is an increasingly important problem in scientific applications where models are implemented as sophisticated computer simulations. As a result, tractable densities are not available, which forces practitioners to rely on approximations during inference. We address the intractability of densities by training a parameterized classifier whose output is used to approximate likelihood ratios between arbitrary model parameters. In turn, we are able to draw posterior samples by plugging this approximator into common Markov chain Monte Carlo samplers such as Metropolis-Hastings and Hamiltonian Monte Carlo. We demonstrate the proposed technique by fitting the generating parameters of implicit models, ranging from a linear probabilistic model to settings in high energy physics with high-dimensional observations. Finally, we discuss several diagnostics to assess the quality of the posterior.


A Python toolkit for likelihood-free inference.

Adversarial Variational Optimization

We introduce Adversarial Variational Optimization (AVO), a likelihood-free inference algorithm for fitting a non-differentiable generative model incorporating ideas from generative adversarial networks, variational optimization and empirical Bayes.