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Marginal likelihood
The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models
A Widely Applicable Bayesian Information Criterion
Categorical Distributions in Natural Language Processing Version 0.1
Binomial and Multinomial Distributions
Bayesian Monte Carlo
Marginal Likelihood
Bayesian Inference
On the Derivation of the Bayesian Information Criterion
Marginal Likelihood from the Gibbs Output Siddhartha Chib Journal Of
Bayesian Analysis of Graphical Models of Marginal Independence for Three Way Contingency Tables
Objective Priors in the Empirical Bayes Framework Arxiv:1612.00064V5 [Stat.ME] 11 May 2020
Estimating the Marginal Likelihood with Integrated Nested Laplace Approximation (INLA)
A Tutorial on Bayesian Multi-Model Linear Regression with BAS and JASP
Bayesian Inference
Bayesian Inference
On the Use of Marginal Posteriors in Marginal Likelihood Estimation Via Importance Sampling
Bayesian Linear Regression
Marginal Likelihoods in Phylogenetics: a Review of Methods and Applications
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Bayesian Linear Regression (Hyperparameter Estimation, Sparse Priors), Bayesian Logistic Regression
Recursive Pathways to Marginal Likelihood Estimation with Prior
CPSC 540: Machine Learning Empirical Bayes
Variational Bayesian Learning of Directed Graphical Models with Hidden Variables
Marginal Likelihoods in Phylogenetics: a Review of Methods and Applications
Bayesian Model Selection Bayesian Model Selection • Suppose We Have Several Models, Each with Potentially Different Numbers of Parameters
Methods for Computing Marginal Data Densities from the Gibbs Output∗
3 Basics of Bayesian Statistics
Selection Properties of Type II Maximum Likelihood (Empirical Bayes) Linear Models with Individual Variance Components for Predictors
Marginal Likelihood Computation for Model Selection and Hypothesis Testing: an Extensive Review
Efficient Approximations for the Marginal Likelihood of Bayesian
Likelihood: Frequentist Vs Bayesian Reasoning
Bayesian Inference for Categorical Data Analysis
Marginal Likelihood from the Metropolis-Hastings Output
Variational Bayesian Monte Carlo
Approximate Inference
A Comparison of Marginal Likelihood Computation Methods
Marginal Likelihood Estimation
An Empirical Bayes Approach to Optimizing Machine Learning Algorithms
New Approaches to Model Selection in Bayesian Mixed Modeling
Bayesian Inference
New Estimators of the Bayes Factor for Models with High-Dimensional Parameter And/Or Latent Variable Spaces
The Bayesian Information Criterion (BIG) for Choos Ing Model M
Marginal Likelihood and Bayes Factors for Dirichlet Process Mixture
The Variational Bayesian EM Algorithm for Incomplete Data: with Application to Scoring Graphical Model Structures
Calibration and Empirical Bayes Variable Selection
A Variational Bayesian Framework for Graphical Models
Bayesian Monte Carlo
Conceptual Foundations: Bayesian Inference
Marginal Likelihood Estimation Via Power Posteriors
A Monte Carlo Method to Compute the Marginal Likelihood in Non Decomposable Graphical Gaussian Models
CS340 Machine Learning Bayesian Statistics 3
Implementation of Gibbs Sampling Within Bayesian Inference and Its Applications in Actuarial Science
Accurate Computation of Marginal Data Densities Using Variational Bayes ∗