Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator

Author:

Metodiev Martin1,Perrot-Dockès Marie2,Ouadah Sarah3,Irons Nicholas J.4,Latouche Pierre1,Raftery Adrian E.4

Affiliation:

1. Université Clermont Auvergne, Laboratoire de Mathématiques Blaise Pascal

2. Université Paris Cité, CNRS, MAP5, F-75006 Paris, France

3. Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay

4. University of Washington, Department of Statistics

Publisher

Institute of Mathematical Statistics

Reference37 articles.

1. Gronau, Q. F., Singmann, H., and Wagenmakers, E.-J. (2020). “bridgesampling: An R Package for Estimating Normalizing Constants.” Journal of Statistical Software, 92(10): 1–29.

2. Kass, R. E. and Raftery, A. E. (1995). “Bayes Factors.” Journal of the American Statistical Association, 90(430): 773–795.

3. Meng, X.-L. and Wong, W. H. (1996). “Simulating Ratios of Normalizing Constants Via a Simple Identity: A Theoretical Exploration.” Statistica Sinica, 6: 831–860.

4. Chib, S. (1995). “Marginal likelihood from the Gibbs output.” Journal of the American Statistical Association, 90: 1313–1321.

5. Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). “Latent Dirichlet allocation.” Journal of Machine Learning Research, 3: 993–1022.

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