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Simulation based sequential Monte Carlo methods for discretely observed Markov processes

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Peter Neal

Computation · Submitted 16 Apr 2014

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

The authors' main claim These innovations substantially improve the efficiency of the SMC algorithm with minimal effect on the speed of the simulation process.

how it is stated · asserted 1.00 read the record →

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arXiv ID1404.4185 (version 1)
Pith IDQRLGEL57, Pith's permanent record of this paper and its review
CitationPeter Neal. "Simulation based sequential Monte Carlo methods for discretely observed Markov processes." arXiv:1404.4185 (2014). https://pith.science/paper/QRLGEL57
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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, 4,838 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.12 · human participants 0.02 · controlled comparison 0.04 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.11 · mentions code or data availability 0.02.

The abstract, sentence by sentence

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

Parameter estimation for discretely observed Markov processes is a challenging problem. However, simulation of Markov processes is straightforward using the Gillespie algorithm. We exploit this ease of simulation to develop an effective sequential Monte Carlo (SMC) algorithm for obtaining samples from the posterior distribution of the parameters. In particular, we introduce two key innovations, coupled simulations, which allow us to study multiple parameter values on the basis of a single simulation, and a simple, yet effective, importance sampling scheme for steering simulations towards the observed data. These innovations substantially improve the efficiency of the SMC algorithm with minimal effect on the speed of the simulation process. The SMC algorithm is successfully applied to two examples, a Lotka-Volterra model and a Repressilator model.

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 Parameter estimation for discretely observed Markov processes is a challenging problem. background 1.00 0.06 0.02 asserted 1.00
s1 However, simulation of Markov processes is straightforward using the Gillespie algorithm. background 0.99 0.08 0.03 asserted 1.00
s2 We exploit this ease of simulation to develop an effective sequential Monte Carlo (SMC) algorithm for obtaining samples from the posterior distribution of the parameters. contribution 0.86 0.94 0.23 asserted 1.00
s3 In particular, we introduce two key innovations, coupled simulations, which allow us to study multiple parameter values on the basis of a single simulation, and a simple, yet effective, importance sampling scheme for steering simulations towards the observed data. contribution 0.92 0.94 0.16 asserted 1.00
s4 These innovations substantially improve the efficiency of the SMC algorithm with minimal effect on the speed of the simulation process. result 0.74 0.89 0.34 asserted 1.00
s5 The SMC algorithm is successfully applied to two examples, a Lotka-Volterra model and a Repressilator model. result 0.66 0.94 0.23 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · 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

Parameter estimation for discretely observed Markov processes is a challenging problem. However, simulation of Markov processes is straightforward using the Gillespie algorithm. We exploit this ease of simulation to develop an effective sequential Monte Carlo (SMC) algorithm for obtaining samples from the posterior distribution of the parameters. In particular, we introduce two key innovations, coupled simulations, which allow us to study multiple parameter values on the basis of a single simulation, and a simple, yet effective, importance sampling scheme for steering simulations towards the observed data. These innovations substantially improve the efficiency of the SMC algorithm with minimal effect on the speed of the simulation process. The SMC algorithm is successfully applied to two examples, a Lotka-Volterra model and a Repressilator model.

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