ArticleBayesian analysis2018
A New Monte Carlo Method for Estimating Marginal Likelihoods.
Article in Bayesian analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 28 citations in OpenAlex.
- Approximating evidence via bounded harmonic means.Statistics and computing · 2026Article
- EXTENDED STOCHASTIC BLOCK MODELS WITH APPLICATION TO CRIMINAL NETWORKS.The annals of applied statistics · 2022Article
- Inflated Density Ratio and Its Variation and Generalization for Computing Marginal Likelihoods.Journal of the Korean Statistical Society · 2020Article
- A Comparison of Monte Carlo Methods for Computing Marginal Likelihoods of Item Response Theory Models.Journal of the Korean Statistical Society · 2019Article
- Bayesian model selection for spatial capture-recapture models.Ecology and evolution · 2019Article
- Marginal Likelihoods in Phylogenetics: A Review of Methods and Applications.Systematic biology · 2019Review
- Assessing Combinability of Phylogenomic Data Using Bayes Factors.Systematic biology · 2019Article
- Thermodynamic integration via differential evolution: A method for estimating marginal likelihoods.Behavior research methods · 2019Article
- Partition Weighted Approach for Estimating the Marginal Posterior Density with Applications.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2019Article
- A New Monte Carlo Method for Estimating Marginal Likelihoods.Bayesian analysis · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Evaluating the marginal likelihood in Bayesian analysis is essential for model selection. Estimators based on a single Markov chain Monte Carlo sample from the posterior distribution include the harmonic mean estimator and the inflated density ratio estimator. We propose a new class of Monte Carlo estimators based on this single Markov chain Monte Carlo sample. This class can be thought of as a generalization of the harmonic mean and inflated density ratio estimators using a partition weighted kernel (likelihood times prior). We show that our estimator is consistent and has better theoretical properties than the harmonic mean and inflated density ratio estimators. In addition, we provide guidelines on choosing optimal weights. Simulation studies were conducted to examine the empirical performance of the proposed estimator. We further demonstrate the desirable features of the proposed estimator with two real data sets: one is from a prostate cancer study using an ordinal probit regression model with latent variables; the other is for the power prior construction from two Eastern Cooperative Oncology Group phase III clinical trials using the cure rate survival model with similar objectives.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.