Marginal Likelihood Computation for Model Selection and Hypothesis Testing: An Extensive Review
From MaRDI portal
Publication:5883296
DOI10.1137/20M1310849OpenAlexW3000534101WikidataQ122235370 ScholiaQ122235370MaRDI QIDQ5883296
David Delgado, Luca Martino, Unnamed Author, Fernando Rodriguez Llorente
Publication date: 30 March 2023
Published in: SIAM Review (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2005.08334
numerical integrationmodel selectionhypothesis testingpartition functionsmarginal likelihoodquadrature rulesBayesian evidencedoubly intractable posteriors
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
Related Items (4)
Priors in Bayesian Deep Learning: A Review ⋮ Target-aware Bayesian inference via generalized thermodynamic integration ⋮ Generalized integral transform and Hamiltonian Monte Carlo for Bayesian structural damage identification ⋮ MCMC-driven importance samplers
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
- Recursive pathways to marginal likelihood estimation with prior-sensitivity analysis
- A multi-point Metropolis scheme with generic weight functions
- On Bayesian model and variable selection using MCMC
- Improving power posterior estimation of statistical evidence
- Bayesian model choice based on Monte Carlo estimates of posterior model probabilities
- Estimating the dimension of a model
- On Monte Carlo methods for estimating ratios of normalizing constants
- Bayesian model averaging: A tutorial. (with comments and a rejoinder).
- A new Monte Carlo method for estimating marginal likelihoods
- A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood
- Compressed Monte Carlo with application in particle filtering
- On the flexibility of the design of multiple try Metropolis schemes
- Probabilistic integration: a role in statistical computation?
- Generalized multiple importance sampling
- Integrated likelihood computation methods
- Layered adaptive importance sampling
- Comparison of Bayesian predictive methods for model selection
- Information criteria and statistical modeling.
- Computing the Bayes factor from a Markov chain Monte Carlo simulation of the posterior distribution
- Following a Moving Target—Monte Carlo Inference for Dynamic Bayesian Models
- Properties of nested sampling
- Statistical Inference, Occam's Razor, and Statistical Mechanics on the Space of Probability Distributions
- Marginal Likelihood from the Gibbs Output
- Adaptive Multiple Importance Sampling
- A Comparison of Marginal Likelihood Computation Methods
- Sequential Monte Carlo Samplers
- Advanced Markov Chain Monte Carlo Methods
- Marginal Likelihood Estimation via Power Posteriors
- Importance-Weighted Marginal Bayesian Posterior Density Estimation
- Computing Bayes Factors by Combining Simulation and Asymptotic Approximations
- Estimating Bayes Factors via Posterior Simulation With the Laplace-Metropolis Estimator
- Adaptive Rejection Metropolis Sampling within Gibbs Sampling
- A sequential particle filter method for static models
- Independent Doubly Adaptive Rejection Metropolis Sampling Within Gibbs Sampling
- Bayesian Measures of Model Complexity and Fit
- Marginal Likelihood From the Metropolis–Hastings Output
- Adaptive Rejection Sampling for Gibbs Sampling
- Bayes Factors
- Minimum description length revisited
- Markov Chain Importance Sampling—A Highly Efficient Estimator for MCMC
- On the marginal likelihood and cross-validation
- The Deviance Information Criterion: 12 Years on
- Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
- A review of Bayesian variable selection methods: what, how and which
- Nested sampling for general Bayesian computation
This page was built for publication: Marginal Likelihood Computation for Model Selection and Hypothesis Testing: An Extensive Review
