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Decisions, Decisions: Noise and its Effects on Integral Monte Carlo Algorithms

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J. D. Doll (Department of Chemistry, Brown University, Providence, RI), D. L. Freeman (Department of Chemistry, University of Rhode Island, Kingston

chem-ph · Submitted 26 May 1994

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

The authors' main claim We show that the effects of introducing unbiased noise into the acceptance/rejection phase of the conventional Metropolis approach are surprisingly modest, and, to a significant degree, largely controllable.

how it is stated · asserted 1.00 read the record →

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arXiv IDchem-ph/9405002 (version 1)
CitationBrown University, D. L. Freeman (Department of Chemistry, J. D. Doll (Department of Chemistry, Kingston, Providence, RI), University of Rhode Island. "Decisions, Decisions: Noise and its Effects on Integral Monte Carlo Algorithms." arXiv:chem-ph/9405002 (1994). https://pith.science/paper/chem-ph/9405002
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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.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,957 input tokens, $0.0001 reported.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.12 · human participants 0.02 · controlled comparison 0.16 · randomized assignment 0.02 · states quantitative result 0.06 · compares with prior results 0.58 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

In the present paper we examine the effects of noise on Monte Carlo algorithms, a problem raised previously by Kennedy and Kuti (Phys. Rev. Lett. {\bf 54}, 2473 (1985)). We show that the effects of introducing unbiased noise into the acceptance/rejection phase of the conventional Metropolis approach are surprisingly modest, and, to a significant degree, largely controllable. We present model condensed phase numerical applications to support these conclusions.

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 In the present paper we examine the effects of noise on Monte Carlo algorithms, a problem raised previously by Kennedy and Kuti (Phys. Rev. Lett. {\bf 54}, 2473 (1985)). background 0.93 0.18 0.03 asserted 0.72
s1 We show that the effects of introducing unbiased noise into the acceptance/rejection phase of the conventional Metropolis approach are surprisingly modest, and, to a significant degree, largely controllable. result 0.99 0.94 0.91 asserted 1.00
s2 We present model condensed phase numerical applications to support these conclusions. method 0.57 0.92 0.04 asserted 0.90

What kind of paper the classifier reports this is: simulation study 0.95 · evidence from simulation or computation 1.00.

What was examined, and what was not

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  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • 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

In the present paper we examine the effects of noise on Monte Carlo algorithms, a problem raised previously by Kennedy and Kuti (Phys. Rev. Lett. {\bf 54}, 2473 (1985)). We show that the effects of introducing unbiased noise into the acceptance/rejection phase of the conventional Metropolis approach are surprisingly modest, and, to a significant degree, largely controllable. We present model condensed phase numerical applications to support these conclusions.

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