A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

As the world becomes increasingly complex, so do the statistical models required to analyse
the challenging problems ahead. For the very first time in a single volume, the Handbook of …

Estimating the parameters of mathematical models is a common problem in almost all
branches of science. However, this problem can prove notably difficult when processes and …

A growing number of generative statistical models do not permit the numerical evaluation of
their likelihood functions. Approximate Bayesian computation has become a popular …

Simulation-based inference (SBI) methods such as approximate Bayesian computation
(ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating …

Computer simulations have proven a valuable tool for understanding complex phenomena
across the sciences. However, the utility of simulators for modelling and forecasting …

Simulator-based models are models for which the likelihood is intractable but simulation of
synthetic data is possible. They are often used to describe complex real-world phenomena …

Kernel mean embeddings are a powerful tool to represent probability distributions over
arbitrary spaces as single points in a Hilbert space. Yet, the cost of computing and storing …

Simulation models, in particular agent-based models, are gaining popularity in economics.
The considerable flexibility they offer, as well as their capacity to reproduce a variety of …

In some misspecified settings, the posterior distribution in Bayesian statistics may lead to
inconsistent estimates. To fix this issue, it has been suggested to replace the likelihood by a …