Pith. Sign in

RECORD

Unbiased Markov chain Monte Carlo for intractable target distributions

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
Lawrence Middleton, George Deligiannidis, Arnaud Doucet, Pierre E. Jacob

Computation · Submitted 23 Jul 2018

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We show how to use coupling techniques to generate unbiased estimators in finite time, building on recent advances for generic MCMC algorithms.

how it is stated · asserted 1.00 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID1807.08691 (version 3)
Pith IDNATHNKFH, Pith's permanent record of this paper and its review
CitationArnaud Doucet, George Deligiannidis, Lawrence Middleton, Pierre E. Jacob. "Unbiased Markov chain Monte Carlo for intractable target distributions." arXiv:1807.08691 (2018). https://pith.science/paper/NATHNKFH
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,681 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.29 · human participants 0.02 · controlled comparison 0.61 · randomized assignment 0.01 · states quantitative result 0.06 · compares with prior results 0.95 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v3. Results and the main claim are underlined; .

Performing numerical integration when the integrand itself cannot be evaluated point-wise is a challenging task that arises in statistical analysis, notably in Bayesian inference for models with intractable likelihood functions. Markov chain Monte Carlo (MCMC) algorithms have been proposed for this setting, such as the pseudo-marginal method for latent variable models and the exchange algorithm for a class of undirected graphical models. As with any MCMC algorithm, the resulting estimators are justified asymptotically in the limit of the number of iterations, but exhibit a bias for any fixed number of iterations due to the Markov chains starting outside of stationarity. This "burn-in" bias is known to complicate the use of parallel processors for MCMC computations. We show how to use coupling techniques to generate unbiased estimators in finite time, building on recent advances for generic MCMC algorithms. We establish the theoretical validity of some of these procedures by extending existing results to cover the case of polynomially ergodic Markov chains. The efficiency of the proposed estimators is compared with that of standard MCMC estimators, with theoretical arguments and numerical experiments including state space models and Ising models.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Performing numerical integration when the integrand itself cannot be evaluated point-wise is a challenging task that arises in statistical analysis, notably in Bayesian inference for models with intractable likelihood functions. background 1.00 0.04 0.03 asserted 1.00
s1 Markov chain Monte Carlo (MCMC) algorithms have been proposed for this setting, such as the pseudo-marginal method for latent variable models and the exchange algorithm for a class of undirected graphical models. background 1.00 0.04 0.02 asserted 0.99
s2 As with any MCMC algorithm, the resulting estimators are justified asymptotically in the limit of the number of iterations, but exhibit a bias for any fixed number of iterations due to the Markov chains starting outside of stationarity. background 0.97 0.06 0.03 asserted 1.00
s3 This "burn-in" bias is known to complicate the use of parallel processors for MCMC computations. background 0.97 0.05 0.02 asserted 1.00
s4 We show how to use coupling techniques to generate unbiased estimators in finite time, building on recent advances for generic MCMC algorithms. contribution 0.91 0.94 0.80 asserted 1.00
s5 We establish the theoretical validity of some of these procedures by extending existing results to cover the case of polynomially ergodic Markov chains. contribution 0.93 0.92 0.12 asserted 1.00
s6 The efficiency of the proposed estimators is compared with that of standard MCMC estimators, with theoretical arguments and numerical experiments including state space models and Ising models. contribution 0.42 0.94 0.13 asserted 0.98

What kind of paper the classifier reports this is: method or tool 0.99 · evidence from proof or derivation 0.77.

What was examined, and what was not

sources read
  • arXiv listing abstract v3
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Performing numerical integration when the integrand itself cannot be evaluated point-wise is a challenging task that arises in statistical analysis, notably in Bayesian inference for models with intractable likelihood functions. Markov chain Monte Carlo (MCMC) algorithms have been proposed for this setting, such as the pseudo-marginal method for latent variable models and the exchange algorithm for a class of undirected graphical models. As with any MCMC algorithm, the resulting estimators are justified asymptotically in the limit of the number of iterations, but exhibit a bias for any fixed number of iterations due to the Markov chains starting outside of stationarity. This "burn-in" bias is known to complicate the use of parallel processors for MCMC computations. We show how to use coupling techniques to generate unbiased estimators in finite time, building on recent advances for generic MCMC algorithms. We establish the theoretical validity of some of these procedures by extending existing results to cover the case of polynomially ergodic Markov chains. The efficiency of the proposed estimators is compared with that of standard MCMC estimators, with theoretical arguments and numerical experiments including state space models and Ising models.

Human review

How Pith checks papers → · Full audit of this paper →

How the literature uses it

5 arXiv papers cite this in 7 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 6 sentences without a confident label

Compares with it 1 paper

What it cites · 6 sentences without a confident label

Uses the method or tool of 1 paper
  • We also consider a variant of the pseudo-marginal approach known as the block pseudo-marginal approach [ref] as well as the exchange algorithm [ref] .

    The Block Pseudo-Marginal Sampler · Context · 0.84

Cites as background 1 paper

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.