Source reconstruction: a statistical mechanics perspective
by Eugene Yee
International Journal of Environment and Pollution (IJEP), Vol. 48, No. 1/2/3/4, 2012

Abstract: A Bayesian inference scheme is used to address the problem of general source reconstruction. To this purpose, starting from a simple, physically motivated structure-based representation for a general source distribution, Bayesian probability theory is used to formulate the full joint posterior probability density function for the parameters in this representation. The exploration of the high-dimensional parameter space using simulated annealing and a Markov chain Monte Carlo sampler is described. The explicit linkage between the computational tools used for posterior sampling and concepts in statistical mechanics is emphasised. The proposed methodology is validated using a real dispersion experiment involving a multiple source release.

Online publication date: Wed, 19-Nov-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Environment and Pollution (IJEP):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com