An efficient interpolation technique for jump proposals in reversible jump Markov chain Monte Carlo calculations

dc.contributor.author	Farr, W. M.	en_US
dc.contributor.author	Mandel, I.	en_US
dc.contributor.author	Stevens, D.	en_US
dc.date.accessioned	2016-06-25T01:57:24Z
dc.date.available	2016-06-25T01:57:24Z
dc.date.issued	2015	en_US
dc.identifier.other	HPU4160256	en_US
dc.identifier.uri	https://lib.hpu.edu.vn/handle/123456789/21760	en_US
dc.description.abstract	Selection among alternative theoretical models given an observed dataset is an important challenge in many areas of physics and astronomy. Reversible-jump Markov chain Monte Carlo (RJMCMC) is an extremely powerful technique for performing Bayesian model selection, but it suffers from a fundamental difficulty and it requires jumps between model parameter spaces, but cannot efficiently explore both parameter spaces at once.	en_US
dc.format.extent	12 p.	en_US
dc.format.mimetype	application/pdf	en_US
dc.language.iso	en	en_US
dc.publisher	The Royal Society	en_US
dc.subject	Astronomy	en_US
dc.subject	Astrophysics	en_US
dc.subject	Statistics	en_US
dc.subject	Markov chain Monte Carlo	en_US
dc.subject	Reversible-jump	en_US
dc.subject	Data analysis	en_US
dc.title	An efficient interpolation technique for jump proposals in reversible jump Markov chain Monte Carlo calculations	en_US
dc.type	Book	en_US
dc.size	1.62MB	en_US
dc.department	Education	en_US