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Computing the marginal likelihood (also called the Bayesian model evidence) is an important task in Bayesian model selection, providing a principled quantitative way to compare models.
Using the ADAP learning algorithm to forecast the onset of diabetes mellitus
Smith, J.W.; Everhart, J.E.; Dickson, W.; Knowler, W.C.; Johannes, R.S · 1988
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Approximate Bayesian inference with the weighted likelihood bootstrap
Newton, M.A.; Raftery, A.E · 1994
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Contribution to the discussion of “Approximate Bayesian inference with the weighted likelihood bootstrap” by Newton MA, Raftery AE
Neal, R.M · 1994
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Bayesian model choice: Asymptotics and exact calculations
Gelfand, A.E.; Dey, D.K · 1994
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Bayesian Theory ; John Wiley & Sons:
Bernardo, J.; Smith, A · 1994
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Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
Green, P.J · 1995
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Nested sampling for general Bayesian computation
Skilling, J · 2006
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Current challenges in Bayesian model choice
Clyde, M.; Berger, J.; Bullard, F.; Ford, E.; Jefferys, W.; Luo, R.; Paulo, R.; Loredo, T · 2007
Cited alongside, same era.
Estimating the evidence—A review
Friel, N.; Wyse, J · 2012
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emcee: The MCMC Hammer
Foreman-Mackey, D.; Hogg, D.W.; Lang, D.; Goodman, J · 2013
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Density estimation using real nvp
Dinh, L.; Sohl-Dickstein, J.; Bengio, S · 2016
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G.; Nalisnick, E.; Rezende, D.J.; Mohamed, S.; Lakshminarayanan, B · 2021
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Nested sampling for physical scientists
Ashton, G.; Bernstein, N.; Buchner, J.; Chen, X.; Csányi, G.; Fowlie, A.; Feroz, F.; Griffiths, M.; Handley, W.; Habeck, M.; et al · 2022
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Bayesian model comparison for simulation-based inference
Spurio Mancini, A.; Docherty, M.M.; Price, M.A.; McEwen, J.D · 2022
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Machine learning assisted Bayesian model comparison: Learnt harmonic mean estimator
McEwen, J.D.; Wallis, C.G.; Price, M.A.; Spurio Mancini, A · 2023
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