Many modern statistical applications involve inference for complex stochastic models, where
it is easy to simulate from the models, but impossible to calculate likelihoods. Approximate …

The main objective of this research paper is to design a system which would generate
multimodal, nonparametric Bayesian model, and multilayered probability latent semantic …

In the following article, we consider approximate Bayesian computation (ABC) for certain
classes of time series models. In particular, we focus upon scenarios where the likelihoods …

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

Abstract Approximate Bayesian Computation (ABC) methods have become a “mainstream”
statistical technique in the past decade, following the realisation by statisticians that they are …

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

This chapter considers the question of whether it may be possible to conduct reliable
approximate Bayesian computation (ABC)-based inference for high-dimensional models or …

Approximate Bayesian Computation (ABC) enables statistical inference in simulatorbased
models whose likelihoods are difficult to calculate but easy to simulate from. ABC constructs …

In the following article, we investigate a particle filter for approximating Feynman–Kac
models with indicator potentials and we use this algorithm within Markov chain Monte Carlo …

Few problems in statistics are as perplexing as variable selection in the presence of very
many redundant covariates. The variable selection problem is most familiar in parametric …