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Estimation of parameters in monte carlo modelling
Author(s) -
Duever T. A.,
Reilly P. M.
Publication year - 1990
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450680621
Subject(s) - monte carlo method , monte carlo molecular modeling , range (aeronautics) , computer science , hybrid monte carlo , particle filter , mathematics , statistical physics , monte carlo integration , markov chain monte carlo , quasi monte carlo method , monte carlo method in statistical physics , algorithm , dynamic monte carlo method , mathematical optimization , statistics , artificial intelligence , physics , materials science , composite material , kalman filter
This paper presents a general method for estimating model parameters from experimental data when the model relating the parameters and input variables to the output responses is a Monte Carlo simulation. From a statistical point of view a Bayesian approach is used in which the distribution of the parameters is handled in discretized form as elements of an array in computer storage. The stochastic nature of the Monte Carlo model allows only an estimate of the distribution to be calculated from which the true distribution must then be estimated. For this purpose an exponentiated polynomial function has been found to be useful. The method provides point estimates as well as joint probability regions. Marginal distributions and distributions of functions of the parameters can also be handled. The motivation for exploring this alternative parameter estimation technique comes from the recognition that for some systems, particularly when the underlying process is stochastic in nature, Monte Carlo simulation often is the most suitable way of modelling. As such, the Monte Carlo approach increases the range of problems which can be handled by mathematical modelling. The technique is applied to the modelling of binary copolymerization. Two models, the Mayo‐Lewis and the Penultimate Group Effects models, are considered and a method for discriminating between these models in the light of sequence distribution data is proposed.