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

Picchini, Umberto LU and Forman, Julie Lyng (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)
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author
organization
publishing date
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
language
English
LU publication?
yes
id
334e0321-8dbc-4995-b374-589b5cd98426 (old id 8220725)
date added to LUP
2015-11-30 08:22:44
date last changed
2017-09-24 03:59:29
@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},
  keyword      = {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},
  volume       = {86},
  year         = {2016},
}