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 …

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 …

This chapter adds coverage of recent developments, particularly on auxiliary likelihood
methods and ABC model choice. It focuses on summary statistic selection methods which …

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

Approximate Bayesian computation (ABC) is a popular technique for analysing data for
complex models where the likelihood function is intractable. It involves using simulation from …

We model the tracking of Bluetooth low-energy (BLE) transmitters as a three layer hidden
Markov model with joint state and parameter estimation. We are after a filtering distribution …

ABSTRACT A computationally simple approach to inference in state space models is
proposed, using approximate Bayesian computation (ABC). ABC avoids evaluation of an …

At least three probabilistic definitions of hidden Markov models (HMMs) have been used
frequently in the literature. Unfortunately, one of these definitions shows fatal flaws, however …

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

We introduce a multivariate hidden Markov model to jointly cluster time‐series observations
with different support, that is, circular and linear. Relying on the general projected normal …