Parameter estimation for random dynamical systems using slice sampling

S.J. Hatjispyros, Theodoros Nicoleris and Stephen G. Walker

Physica A: Statistical Mechanics and its Applications, 2007, vol. 381, issue C, 71-81

Abstract: We provide details on the full reconstruction of the dynamic equations from measured time series data, given the general class of the underlying physical process. Our results can be used by researchers in physical modelling and statistical mechanics interested in an efficient estimation of low dimensional models, incorporating dynamic as well as observational noise. Our approach is Bayesian, based on an auxiliary variables algorithm that is fast and accurate, and direct, in the sense that only uniform distributions need to be sampled. This method is simpler than other Bayesian approaches where one has to sample from non-standard–unknown distributions using MCMC methods.

Keywords: Slice sampler; Random dynamical systems (search for similar items in EconPapers)
Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437107002993
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:381:y:2007:i:c:p:71-81

DOI: 10.1016/j.physa.2007.03.013

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().