Results 11 to 20 of about 122,222 (300)
LoRaD: marginal likelihood estimation [PDF]
, 2022 LoRaD: Marginal Likelihood from a Single Posterior ...
Analisa Milkey (12676367)
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Marginal Maximum Likelihood Estimation of Item Response Models in R [PDF]
Journal of Statistical Software, 2007 Item response theory (IRT) models are a class of statistical models used by researchers to describe the response behaviors of individuals to a set of categorically scored items.
Matthew S. Johnson
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Marginal Likelihood Estimation via Power Posteriors [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2008 SummaryModel choice plays an increasingly important role in statistics. From a Bayesian perspective a crucial goal is to compute the marginal likelihood of the data for a given model. However, this is typically a difficult task since it amounts to integrating over all model parameters.
Friel, Nial, Pettitt, Tony
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Bayesian Model Selection, the Marginal Likelihood, and Generalization [PDF]
CoRR, 2022 How do we compare between hypotheses that are entirely consistent with observations? The marginal likelihood (aka Bayesian evidence), which represents the probability of generating our observations from a prior, provides a distinctive approach to this foundational question, automatically encoding Occam's razor.
Sanae Lotfi +4 more
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On marginal likelihood computation in change-point models [PDF]
Computational Statistics & Data Analysis, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Luc Bauwens, Jeroen V. K. Rombouts
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Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models [PDF]
Journal of Causal Inference, 2014 This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention
Petersen Maya +5 more
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Adaptive MCMC for Bayesian Variable Selection in Generalised Linear Models and Survival Models
Entropy, 2023 Developing an efficient computational scheme for high-dimensional Bayesian variable selection in generalised linear models and survival models has always been a challenging problem due to the absence of closed-form solutions to the marginal likelihood ...
Xitong Liang +2 more
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Bayesian model evidence as a practical alternative to deviance information criterion [PDF]
Royal Society Open Science, 2018 While model evidence is considered by Bayesian statisticians as a gold standard for model selection (the ratio in model evidence between two models giving the Bayes factor), its calculation is often viewed as too computationally demanding for many ...
C. M. Pooley, G. Marion
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A Hybrid Approximation to the Marginal Likelihood
, 2021 Computing the marginal likelihood or evidence is one of the core challenges in Bayesian analysis. While there are many established methods for estimating this quantity, they predominantly rely on using a large number of posterior samples obtained from a Markov Chain Monte Carlo (MCMC) algorithm.
Eric Chuu +2 more
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fastSTRUCTURE marginal likelihood values. [PDF]
, 2022 Marginal likelihood increased until K = 5, with the largest increase occurring between K = 1–3. The data underlying this figure can be found in DOI: 10.5281/zenodo.5775265. (TIFF)
Lotus A. Lofgren (9253428) +3 more
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