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Practical Consequences of the Bias in the Laplace Approximation to Marginal Likelihood for Hierarchical Models [PDF]
EntropyDue to the high dimensional integration over latent variables, computing marginal likelihood and posterior distributions for the parameters of a general hierarchical model is a difficult task.
Subhash R. Lele +2 more
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Marginal likelihood estimate comparisons to obtain optimal species delimitations in Silene sect. Cryptoneurae (Caryophyllaceae). [PDF]
PLoS ONE, 2014 Coalescent-based inference of phylogenetic relationships among species takes into account gene tree incongruence due to incomplete lineage sorting, but for such methods to make sense species have to be correctly delimited. Because alternative assignments
Zeynep Aydin +3 more
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Stepwise Signal Extraction via Marginal Likelihood. [PDF]
J Am Stat Assoc, 2016 This article studies the estimation of a stepwise signal. To determine the number and locations of change-points of the stepwise signal, we formulate a maximum marginal likelihood estimator, which can be computed with a quadratic cost using dynamic programming.
Du C, Kao CM, Kou SC.
europepmc +4 more sources
On the use of marginal posteriors in marginal likelihood estimation via importance sampling [PDF]
Computational Statistics and Data Analysis, 2014 We investigate the efficiency of a marginal likelihood estimator where the product of the marginal posterior distributions is used as an importance-sampling function. The approach is generally applicable to multi-block parameter vector settings, does not require additional Markov Chain Monte Carlo (MCMC) sampling and is not dependent on the type of ...
Konstantinos Perrakis +2 more
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On the marginal likelihood and cross-validation [PDF]
Biometrika, 2020 SummaryIn Bayesian statistics, the marginal likelihood, also known as the evidence, is used to evaluate model fit as it quantifies the joint probability of the data under the prior. In contrast, non-Bayesian models are typically compared using cross-validation on held-out data, either through $k$-fold partitioning or leave-$p$-out subsampling.
Fong, E, Holmes, CC
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Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics [PDF]
PeerJ, 2021 In Bayesian phylogenetic inference, marginal likelihoods can be estimated using several different methods, including the path-sampling or stepping-stone-sampling algorithms.
Sebastian Höhna +2 more
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Bayesian Inference for the Loss Models via Mixture Priors
Risks, 2023 Constructing an accurate model for insurance losses is a challenging task. Researchers have developed various methods to model insurance losses, such as composite models. Composite models combine two distributions: one for part of the data with small and
Min Deng, Mostafa S. Aminzadeh
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An MM Algorithm for the Frailty-Based Illness Death Model with Semi-Competing Risks Data
Mathematics, 2022 For analyzing multiple events data, the illness death model is often used to investigate the covariate–response association for its easy and direct interpretation as well as the flexibility to accommodate the within-subject dependence.
Xifen Huang +4 more
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On Masked Pre-training and the Marginal Likelihood
Advances in Neural Information Processing Systems 36, 2023 Masked pre-training removes random input dimensions and learns a model that can predict the missing values. Empirical results indicate that this intuitive form of self-supervised learning yields models that generalize very well to new domains. A theoretical understanding is, however, lacking.
Moreno Muñoz, Pablo +2 more
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Universe, 2021 The likelihood ratio for a continuous gravitational wave signal is viewed geometrically as a function of the orientation of two vectors; one representing the optimal signal-to-noise ratio, and the other representing the maximised likelihood ratio or F ...
Karl Wette
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