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Computing the marginal likelihood (ML) of a model requires marginalizing out all of the parameters and latent variables, a difficult high-dimensional summation or integration problem.
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Approximate Bayesian inference with the weighted likelihood bootstrap
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Fractional Bayes factors for model comparison
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The intrinsic bayes factor for model selection and prediction
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J. G. Propp and D. B. Wilson · 1996
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The minimum description length principle in coding and modeling
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Andrew Gelman and Xiao-Li Meng · 1998
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Comparison of approximate methods for handling hyperparameters
D. J. C. MacKay · 1999
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Erroneous results in ”Marginal likelihood from the Gibbs output”
R. M. Neal · 1999
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H. Attias · 2000
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Propagation algorithms for variational Bayesian learning
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The harmonic mean of the likelihood: worst Monte Carlo method ever
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Probabilistic matrix factorization
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Accelerated sampling for the Indian buffet process
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Particle learning and smoothing
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Exploiting compositionality to explore a large space of model structures
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Practical bayesian optimization of machine learning algorithms
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Annealing between distributions by averaging moments
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Testing MCMC code
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Accurate and conservative estimates of MRF log-likelihood using reverse annealing
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