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A practical implementation of this procedure requires a fully Bayesian analysis for both the state vector and the unknown hyperparameters and is carried out by Markov chain Monte Carlo methods. An application to some non-standard situations such as testing hypotheses on the boundary of the parameter space, discriminating non-nested models and discrimination of more than two models is discussed in detail.<\/jats:p>","DOI":"10.1111\/j.2517-6161.1995.tb02027.x","type":"journal-article","created":{"date-parts":[[2018,12,7]],"date-time":"2018-12-07T06:30:39Z","timestamp":1544164239000},"page":"237-246","source":"Crossref","is-referenced-by-count":49,"title":["Bayesian Model Discrimination and Bayes Factors for Linear Gaussian State Space Models"],"prefix":"10.1093","volume":"57","author":[{"given":"Sylvia","family":"Fr\u00fchwirth-Schnatter","sequence":"first","affiliation":[{"name":"Vienna University of Economics and Business Administration , Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,12,5]]},"reference":[{"key":"2024090922050060300_rssb02027-cit-0001","first-page":"267","article-title":"Information theory and an extension of the maximum likelihood principle","volume-title":"Proc. 2nd Int. 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