Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation

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Standard

Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation. / Picchini, Umberto; Forman, Julie Lyng.

I: Journal of Statistical Computation and Simulation, Bind 86, Nr. 1, 2016, s. 195–213.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Picchini, U & Forman, JL 2016, 'Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation', Journal of Statistical Computation and Simulation, bind 86, nr. 1, s. 195–213. https://doi.org/10.1080/00949655.2014.1002101

APA

Picchini, U., & Forman, J. L. (2016). Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation. Journal of Statistical Computation and Simulation, 86(1), 195–213. https://doi.org/10.1080/00949655.2014.1002101

Vancouver

Picchini U, Forman JL. Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation. Journal of Statistical Computation and Simulation. 2016;86(1):195–213. https://doi.org/10.1080/00949655.2014.1002101

Author

Picchini, Umberto ; Forman, Julie Lyng. / Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation. I: Journal of Statistical Computation and Simulation. 2016 ; Bind 86, Nr. 1. s. 195–213.

Bibtex

@article{adeffb1ec0ff47e6837646b7236ae61e,
title = "Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation",
abstract = "In recent years, dynamical modelling has been provided with a range of breakthrough methods to perform exact Bayesian inference. However, it is often computationally unfeasible to apply exact statistical methodologies in the context of large data sets and complex models. This paper considers a nonlinear stochastic differential equation model observed with correlated measurement errors and an application to protein folding modelling. An approximate Bayesian computation (ABC)-MCMC algorithm is suggested to allow inference for model parameters within reasonable time constraints. The ABC algorithm uses simulations of ‘subsamples’ from the assumed data-generating model as well as a so-called ‘early-rejection’ strategy to speed up computations in the ABC-MCMC sampler. Using a considerate amount of subsamples does not seem to degrade the quality of the inferential results for the considered applications. A simulation study is conducted to compare our strategy with exact Bayesian inference, the latter resulting two orders of magnitude slower than ABC-MCMC for the considered set-up. Finally, the ABC algorithm is applied to a large size protein data. The suggested methodology is fairly general and not limited to the exemplified model and data.",
keywords = "likelihood-free inference, MCMC, protein folding, stochastic differential equation",
author = "Umberto Picchini and Forman, {Julie Lyng}",
year = "2016",
doi = "10.1080/00949655.2014.1002101",
language = "English",
volume = "86",
pages = "195–213",
journal = "Journal of Statistical Computation and Simulation",
issn = "0094-9655",
publisher = "Taylor & Francis",
number = "1",

}

RIS

TY - JOUR

T1 - Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation

AU - Picchini, Umberto

AU - Forman, Julie Lyng

PY - 2016

Y1 - 2016

N2 - In recent years, dynamical modelling has been provided with a range of breakthrough methods to perform exact Bayesian inference. However, it is often computationally unfeasible to apply exact statistical methodologies in the context of large data sets and complex models. This paper considers a nonlinear stochastic differential equation model observed with correlated measurement errors and an application to protein folding modelling. An approximate Bayesian computation (ABC)-MCMC algorithm is suggested to allow inference for model parameters within reasonable time constraints. The ABC algorithm uses simulations of ‘subsamples’ from the assumed data-generating model as well as a so-called ‘early-rejection’ strategy to speed up computations in the ABC-MCMC sampler. Using a considerate amount of subsamples does not seem to degrade the quality of the inferential results for the considered applications. A simulation study is conducted to compare our strategy with exact Bayesian inference, the latter resulting two orders of magnitude slower than ABC-MCMC for the considered set-up. Finally, the ABC algorithm is applied to a large size protein data. The suggested methodology is fairly general and not limited to the exemplified model and data.

AB - In recent years, dynamical modelling has been provided with a range of breakthrough methods to perform exact Bayesian inference. However, it is often computationally unfeasible to apply exact statistical methodologies in the context of large data sets and complex models. This paper considers a nonlinear stochastic differential equation model observed with correlated measurement errors and an application to protein folding modelling. An approximate Bayesian computation (ABC)-MCMC algorithm is suggested to allow inference for model parameters within reasonable time constraints. The ABC algorithm uses simulations of ‘subsamples’ from the assumed data-generating model as well as a so-called ‘early-rejection’ strategy to speed up computations in the ABC-MCMC sampler. Using a considerate amount of subsamples does not seem to degrade the quality of the inferential results for the considered applications. A simulation study is conducted to compare our strategy with exact Bayesian inference, the latter resulting two orders of magnitude slower than ABC-MCMC for the considered set-up. Finally, the ABC algorithm is applied to a large size protein data. The suggested methodology is fairly general and not limited to the exemplified model and data.

KW - likelihood-free inference

KW - MCMC

KW - protein folding

KW - stochastic differential equation

U2 - 10.1080/00949655.2014.1002101

DO - 10.1080/00949655.2014.1002101

M3 - Journal article

VL - 86

SP - 195

EP - 213

JO - Journal of Statistical Computation and Simulation

JF - Journal of Statistical Computation and Simulation

SN - 0094-9655

IS - 1

ER -

ID: 135268064