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KOBAYASHI GENYA
Department Undergraduate School , School of Commerce Position Professor |
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| Language | English |
| Publication Date | 2014/12 |
| Type | Academic Journal |
| Peer Review | Peer reviewed |
| Title | A transdimensional approximate Bayesian computation using the pseudo-marginal approach for model choice |
| Contribution Type | Sole-authored |
| Journal | COMPUTATIONAL STATISTICS & DATA ANALYSIS |
| Journal Type | Another Country |
| Publisher | ELSEVIER SCIENCE BV |
| Volume, Issue, Page | 80,pp.167-183 |
| Author and coauthor | Genya Kobayashi |
| Details | When the likelihood functions are either unavailable analytically or are computationally cumbersome to evaluate, it is impossible to implement conventional Bayesian model Choice methods. Instead, approximate Bayesian computation (ABC) or the likelihood-free method can be used in order to avoid direct evaluation of the intractable likelihoods. This paper proposes a new Markov chain Monte Carlo (MCMC) method for model choice. This method is based on the pseudo-marginal approach and is appropriate for situations where the likelihood functions for the competing models are intractable. This method proposes jumps between the models with different dimensionalities without matching the dimensionalities. Therefore, it enables the construction of a flexible proposal distribution. The proposal distribution used in this paper is convenient to implement and works well in the context of ABC. |
| DOI | 10.1016/j.csda.2014.06.025 |
| ISSN | 0167-9473 |