Bayesian conformational analysis of ring molecules through reversible jump MCMC | Zendy

Nolsøe Kim | Zendy; Kessler Mathieu | Zendy; Pérez José | Zendy; Madsen Henrik | Zendy

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Bayesian conformational analysis of ring molecules through reversible jump MCMC

Author(s) -

Nolsøe Kim,

Kessler Mathieu,

Pérez José,

Madsen Henrik

Publication year - 2005

Publication title -

journal of chemometrics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.47

H-Index - 92

eISSN - 1099-128X

pISSN - 0886-9383

DOI - 10.1002/cem.943

Subject(s) - reversible jump markov chain monte carlo , markov chain monte carlo , bayesian inference , bayesian probability , computer science , jump , data set , inference , set (abstract data type) , ring (chemistry) , algorithm , posterior probability , statistical physics , octane , markov chain , chemistry , artificial intelligence , thermodynamics , machine learning , physics , quantum mechanics , programming language , organic chemistry

In this paper, we address the problem of classifying the conformations of m ‐membered rings using experimental observations obtained by crystal structure analysis. We formulate a model for the data generation mechanism that consists in a multidimensional mixture model. We perform inference for the proportions and the components in a Bayesian framework, implementing a Markov chain Monte Carlo (MCMC) reversible jump algorithm to obtain samples of the posterior distributions. The method is illustrated on a simulated data set and on real data corresponding to cyclo‐octane structures. Copyright © 2005 John Wiley & Sons, Ltd.

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