Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …

Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Many domains of science have developed complex simulations to describe phenomena of
interest. While these simulations provide high-fidelity models, they are poorly suited for …

Recent advances in probabilistic modelling have led to a large number of simulation-based
inference algorithms which do not require numerical evaluation of likelihoods. However, a …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

How can one perform Bayesian inference on stochastic simulators with intractable
likelihoods? A recent approach is to learn the posterior from adaptively proposed …

Many of the statistical models that could provide an accurate, interesting, and testable
explanation for the structure of a data set turn out to have intractable likelihood functions …

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

Likelihood-free methods perform parameter inference in stochastic simulator models where
evaluating the likelihood is intractable but sampling synthetic data is possible. One class of …

Simulators often provide the best description of real-world phenomena. However, the
probability density that they implicitly define is often intractable, leading to challenging …