Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation
(2016) In Journal of Statistical Computation and Simulation 86(1). p.195-213- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8220725
- author
- Picchini, Umberto LU and Forman, Julie Lyng
- organization
- publishing date
- 2016
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- likelihood-free inference, MCMC, protein folding, stochastic, differential equation, 62M09, 62F15, 60J60, 92C40
- in
- Journal of Statistical Computation and Simulation
- volume
- 86
- issue
- 1
- pages
- 195 - 213
- publisher
- Taylor & Francis
- external identifiers
-
- wos:000362122300013
- scopus:84942980498
- ISSN
- 1563-5163
- DOI
- 10.1080/00949655.2014.1002101
- project
- Stochastic modelling of protein folding and likelihood-free statistical inference methods
- language
- English
- LU publication?
- yes
- id
- 334e0321-8dbc-4995-b374-589b5cd98426 (old id 8220725)
- date added to LUP
- 2016-04-01 13:41:05
- date last changed
- 2022-03-29 08:43:26
@article{334e0321-8dbc-4995-b374-589b5cd98426,
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.}},
author = {{Picchini, Umberto and Forman, Julie Lyng}},
issn = {{1563-5163}},
keywords = {{likelihood-free inference; MCMC; protein folding; stochastic; differential equation; 62M09; 62F15; 60J60; 92C40}},
language = {{eng}},
number = {{1}},
pages = {{195--213}},
publisher = {{Taylor & Francis}},
series = {{Journal of Statistical Computation and Simulation}},
title = {{Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation}},
url = {{http://dx.doi.org/10.1080/00949655.2014.1002101}},
doi = {{10.1080/00949655.2014.1002101}},
volume = {{86}},
year = {{2016}},
}