Inference for SDE models via Approximate Bayesian Computation
(2014) In Journal of Computational and Graphical Statistics 23(4). p.1080-1100- Abstract
- Models defined by stochastic differential equations (SDEs) allow for the representation of random variability in dynamical systems. The relevance of this class of models is growing in many applied research areas and is already a standard tool to model e.g. financial, neuronal and population growth dynamics. However inference for multidimensional SDE models is still very challenging, both computationally and theoretically. Approximate Bayesian computation (ABC) allow to perform Bayesian inference for models which are sufficiently complex that the likelihood function is either analytically unavailable or computationally prohibitive to evaluate. A computationally efficient ABC-MCMC algorithm is proposed, halving the running time in our... (More)
- Models defined by stochastic differential equations (SDEs) allow for the representation of random variability in dynamical systems. The relevance of this class of models is growing in many applied research areas and is already a standard tool to model e.g. financial, neuronal and population growth dynamics. However inference for multidimensional SDE models is still very challenging, both computationally and theoretically. Approximate Bayesian computation (ABC) allow to perform Bayesian inference for models which are sufficiently complex that the likelihood function is either analytically unavailable or computationally prohibitive to evaluate. A computationally efficient ABC-MCMC algorithm is proposed, halving the running time in our simulations. Focus is on the case where the SDE describes latent dynamics in state-space models; however the methodology is not limited to the state-space framework. Simulation studies for a pharmacokinetics/pharmacodynamics model and for stochastic chemical reactions are considered and a Matlab package implementing our ABC-MCMC algorithm is provided. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4215970
- author
- Picchini, Umberto LU
- organization
- publishing date
- 2014
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- early–rejection MCMC, likelihood-free inference, state-space model, stochastic differential equation, stochastic chemical reaction
- in
- Journal of Computational and Graphical Statistics
- volume
- 23
- issue
- 4
- pages
- 1080 - 1100
- publisher
- American Statistical Association
- external identifiers
-
- wos:000343314300010
- scopus:84908075115
- ISSN
- 1537-2715
- DOI
- 10.1080/10618600.2013.866048
- language
- English
- LU publication?
- yes
- additional info
- Accepted author version posted online 18 Dec 2013 at Taylor & Francis Online.
- id
- fe967cde-bb25-46e3-9134-8764fefab684 (old id 4215970)
- date added to LUP
- 2016-04-01 09:54:30
- date last changed
- 2022-04-12 00:01:54
@article{fe967cde-bb25-46e3-9134-8764fefab684,
abstract = {{Models defined by stochastic differential equations (SDEs) allow for the representation of random variability in dynamical systems. The relevance of this class of models is growing in many applied research areas and is already a standard tool to model e.g. financial, neuronal and population growth dynamics. However inference for multidimensional SDE models is still very challenging, both computationally and theoretically. Approximate Bayesian computation (ABC) allow to perform Bayesian inference for models which are sufficiently complex that the likelihood function is either analytically unavailable or computationally prohibitive to evaluate. A computationally efficient ABC-MCMC algorithm is proposed, halving the running time in our simulations. Focus is on the case where the SDE describes latent dynamics in state-space models; however the methodology is not limited to the state-space framework. Simulation studies for a pharmacokinetics/pharmacodynamics model and for stochastic chemical reactions are considered and a Matlab package implementing our ABC-MCMC algorithm is provided.}},
author = {{Picchini, Umberto}},
issn = {{1537-2715}},
keywords = {{early–rejection MCMC; likelihood-free inference; state-space model; stochastic differential equation; stochastic chemical reaction}},
language = {{eng}},
number = {{4}},
pages = {{1080--1100}},
publisher = {{American Statistical Association}},
series = {{Journal of Computational and Graphical Statistics}},
title = {{Inference for SDE models via Approximate Bayesian Computation}},
url = {{http://dx.doi.org/10.1080/10618600.2013.866048}},
doi = {{10.1080/10618600.2013.866048}},
volume = {{23}},
year = {{2014}},
}