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REPARAMETRIZATION ASPECTS OF NUMERICAL BAYESIAN METHODOLOGY FOR AUTOREGRESSIVE MOVING‐AVERAGE MODELS
Author(s) -
Marriott J. M.,
Smith A. F. M.
Publication year - 1992
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1992.tb00111.x
Subject(s) - mathematics , autoregressive model , numerical integration , bayesian probability , context (archaeology) , bayes' theorem , transformation (genetics) , autoregressive–moving average model , bayes factor , bayesian inference , series (stratigraphy) , inference , econometrics , statistics , artificial intelligence , computer science , paleontology , mathematical analysis , biochemistry , chemistry , gene , biology
Abstract. Within the context of likelihood and Bayes approaches to inference in autoregressive moving‐average (ARMA) time series models, previous ideas on parameter transformation and numerical integration for implementing Bayesian procedures are reviewed. Some novel transformation ideas are introduced and their role in an efficient numerical integration approach is examined. Some comparisons of the effectivesness of different numerical integration strategies are made.