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

Abstract Approximate Bayesian Computation (ABC) techniques are a suite of model fitting
methods which can be implemented without a using likelihood function. In order to use ABC …

Modern machine learning algorithms excel in a great variety of tasks, but at the same time, it
is also known that those complex models need to deal with uncertainty from different …

A growing range of generative statistical models are such the numerical evaluation of their
likelihood functions is intractable. Approximate Bayesian computation and indirect inference …

Secure power system operation relies on accurate steady-state and dynamic system
models. It is thus crucial to carefully validate the models in power systems, in particular the …

This chapter surveys the various forms of approximate Bayesian computation (ABC)
algorithms that have been developed to sample from pABC. The earliest ABC samplers …

Likelihood-free methods such as approximate Bayesian computation (ABC) have extended
the reach of statistical inference to problems with computationally intractable likelihoods …

Abstract Particle Markov chain Monte Carlo (pMCMC) is now a popular method for
performing Bayesian statistical inference on challenging state space models (SSMs) with …

Many complicated Bayesian posteriors are difficult to approximate by either sampling or
optimisation methods. Therefore we propose a novel approach combining features of both …

Approximate Bayesian computation (ABC) is the most popular approach to inferring
parameters in the case where the data model is specified in the form of a simulator. It is not …