# Annealed importance sampling

@article{Neal2001AnnealedIS, title={Annealed importance sampling}, author={Radford M. Neal}, journal={Statistics and Computing}, year={2001}, volume={11}, pages={125-139} }

Simulated annealing—moving from a tractable distribution to a distribution of interest via a sequence of intermediate distributions—has traditionally been used as an inexact method of handling isolated modes in Markov chain samplers. [... ] Key Method The Markov chain aspect allows this method to perform acceptably even for high-dimensional problems, where finding good importance sampling distributions would otherwise be very difficult, while the use of importance weights ensures that the estimates found… Expand

## 1,217 Citations

Importance tempering

- MathematicsStat. Comput.
- 2010

A new optimal method for combining multiple IS estimators is derived and it is proved that the resulting estimator has a highly desirable property related to the notion of effective sample size.

Importance Sampling Schemes for Evidence Approximation in Mixture Models

- Computer Science
- 2013

Two importance sampling schemes are proposed through choices for the importance function; a MLE proposal and a Rao-Blackwellised importance function, called dual importance sampling, which is demonstrated to be a valid estimator of the evidence and to increase the statistical efficiency of estimates.

ANNEALED IMPORTANCE SAMPLING MEETS SCORE MATCHING

- Mathematics
- 2022

Annealed Importance Sampling (AIS) is one of the most effective methods for marginal likelihood estimation. It relies on a sequence of distributions interpolating between a tractable initial…

Importance sampling type correction of Markov chain Monte Carlo and exact approximations

- Mathematics, Computer Science
- 2016

This work uses an importance sampling (IS) type correction of approximate Markov chain Monte Carlo (MCMC) output in order to provide consistent estimators and proves strong consistency of the suggested estimators under mild assumptions, and provides central limit theorems with expressions for asymptotic variances.

Revisiting the balance heuristic for estimating normalising constants

- Computer Science
- 2019

The focus of this work lies within the previous context, exploring some improvements and variations of the balance heuristic via a novel extended-space representation of the estimator, leading to straightforward annealing schemes for variance reduction purposes.

An Implementation of the Annealed Importance Sampling Algorithm for Model Comparison by

- Computer Science
- 2008

The Annealed Importance Sampling algorithm for estimating marginal likelihoods, which is a vital task in model comparison, has been implemented and can be installed as an additional plugin for BioBayes.

Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain Decomposition

- Computer ScienceArXiv
- 2018

This work combines Langevin diffusion with simulated tempering to create a Markov chain that mixes more rapidly by transitioning between different temperatures of the distribution, and introduces novel techniques for proving spectral gaps based on decomposing the action of the generator of the diffusion.

Stochastic Gradient Annealed Importance Sampling for Efficient Online Marginal Likelihood Estimation †

- Computer Science, MathematicsEntropy
- 2019

The resulting stochastic gradient annealed importance sampling (SGAIS) technique enables us to estimate the marginal likelihood of a number of models considerably faster than traditional approaches, with no noticeable loss of accuracy.

Advances in Markov chain Monte Carlo methods

- Computer Science
- 2007

This thesis proposes and investigates several new Monte Carlo algorithms, both for evaluating normalizing constants and for improved sampling of distributions, and develops novel exact-sampling-based MCMC methods, the Exchange Algorithm and Latent Histories.

Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo

- Computer ScienceScandinavian Journal of Statistics
- 2020

The IS approach provides a natural alternative to delayed acceptance (DA) pseudo‐marginal/particle MCMC, and has many advantages over DA, including a straightforward parallelization and additional flexibility in MCMC implementation, and is often competitive even without parallelization.

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