Results 21 to 30 of about 122,222 (300)
bridgesampling: An R Package for Estimating Normalizing Constants
Journal of Statistical Software, 2020 Statistical procedures such as Bayes factor model selection and Bayesian model averaging require the computation of normalizing constants (e.g., marginal likelihoods).
Quentin F. Gronau +2 more
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A topology-marginal composite likelihood via a generalized phylogenetic pruning algorithm
Algorithms for Molecular Biology, 2023 Bayesian phylogenetics is a computationally challenging inferential problem. Classical methods are based on random-walk Markov chain Monte Carlo (MCMC), where random proposals are made on the tree parameter and the continuous parameters simultaneously ...
Seong-Hwan Jun +8 more
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Outlier-Robust Surrogate Modeling of Ion–Solid Interaction Simulations
Entropy, 2023 Data for complex plasma–wall interactions require long-running and expensive computer simulations. Furthermore, the number of input parameters is large, which results in low coverage of the (physical) parameter space.
Roland Preuss, Udo von Toussaint
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Computing expectations and marginal likelihoods for permutations [PDF]
Computational Statistics, 2019 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ben Powell, Paul A. Smith
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A Variant of AIC Based on the Bayesian Marginal Likelihood [PDF]
Sankhya B, 2018 We propose information criteria that measure the prediction risk of a predictive density based on the Bayesian marginal likelihood from a frequentist point of view. We derive criteria for selecting variables in linear regression models, assuming a prior distribution of the regression coefficients.
Kawakubo, Yuki +2 more
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Bayesian Inference for Seasonal ARMA Models [PDF]
The Egyptian Statistical Journal, 1987 An essential ingredient of any time series anatysis is the estimation of the modcl parameters. The main objective of this paper is to develop a convenient Rayesian technique for estimation which can be used to analyze ‘seasonal autoregressive moving ...
Samir Shaarawy, Mohamed Ismail
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Marginalization using the metric of likelihood
AIP Conference Proceedings, 2001 Although the likelihood function is normalizeable with respect to the data there is no guarantee that the same holds with respect to the model parameters. This may lead to singularities in the expectation value integral of these parameters, especially if the prior information is not sufficient to take care of finite integral values.
Preuss, R., Dose, V.
+6 more sources
Logarithmic marginal likelihood of different estimation models. [PDF]
, 2023 Logarithmic marginal likelihood of different estimation models.
Jiamu Hu (17334114)
core +1 more source
A bimatrix variate gamma distribution
Results in Applied Mathematics, 2022 In this article, a bimatrix gamma distributions is introduced. Various mathematical properties of the proposed distribution like marginal distributions, expected values, entropies, and moment generating function are derived.
Maryam Rafiei +4 more
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Improved Laplace approximation for marginal likelihoods
Electronic Journal of Statistics, 2016 Statistical applications often involve the calculation of intractable multidimensional integrals. The Laplace formula is widely used to approximate such integrals. However, in high-dimensional or small sample size problems, the shape of the integrand function may be far from that of the Gaussian density, and thus the standard Laplace approximation can ...
RULI, ERLIS +2 more
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