AI Chat AI Image Generator AI Video Text to Speech

ABC Samplers

02/26/2018

∙

by Y. Fan, et al.

∙

∙

This Chapter, "ABC Samplers", is to appear in the forthcoming Handbook of Approximate Bayesian Computation (2018). It details the main ideas and algorithms used to sample from the ABC approximation to the posterior distribution, including methods based on rejection/importance sampling, MCMC and sequential Monte Carlo.

Y. Fan
5 publications
S. A. Sisson
5 publications

page 1

page 2

page 3

page 4

research

∙ 02/27/2018

Overview of Approximate Bayesian Computation

This Chapter, "Overview of Approximate Bayesian Computation", is to appe...

0 S. A. Sisson, et al. ∙

research

∙ 06/07/2016

Measuring the reliability of MCMC inference with bidirectional Monte Carlo

Markov chain Monte Carlo (MCMC) is one of the main workhorses of probabi...

0 Roger B. Grosse, et al. ∙

research

∙ 11/21/2019

Parallelising MCMC via Random Forests

For Bayesian computation in big data contexts, the divide-and-conquer MC...

0 Wu Changye, et al. ∙

research

∙ 09/11/2018

Rethinking the Effective Sample Size

The effective sample size (ESS) is widely used in sample-based simulatio...

0 Victor Elvira, et al. ∙

research

∙ 06/22/2018

Tensor Monte Carlo: particle methods for the GPU era

Multi-sample objectives improve over single-sample estimates by giving t...

0 Laurence Aitchison, et al. ∙

research

∙ 10/26/2020

Bayesian Fusion of Data Partitioned Particle Estimates

We present a Bayesian data fusion method to approximate a posterior dist...

0 Caleb Miller, et al. ∙

research

∙ 10/03/2022

Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling

Bayesian neural networks (BNNs) have received an increased interest in t...

0 Yunshi Huang, et al. ∙