ArticleProceedings of the National Academy of Sciences of the United States of America2013
Bayesian computation via empirical likelihood.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Functional connectivity across the human subcortical auditory system using an autoregressive matrix-Gaussian copula graphical model approach with partial correlations.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Evaluation of spatial Bayesian Empirical Likelihood models in analysis of small area data.PloS one · 2022Article
- A unified Bayesian framework for exact inference of area under the receiver operating characteristic curve.Statistical methods in medical research · 2021Article
- Bayesian analysis of immigration in Europe with generalized logistic regression.Journal of applied statistics · 2020Article
- Accelerating Bayesian inference for evolutionary biology models.Bioinformatics (Oxford, England) · 2017Article
- Approximate Bayesian inference for complex ecosystems.F1000prime reports · 2014Review
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3 authors.
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Abstract
Approximate Bayesian computation has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However, the well-established statistical method of empirical likelihood provides another route to such settings that bypasses simulations from the model and the choices of the approximate Bayesian computation parameters (summary statistics, distance, tolerance), while being convergent in the number of observations. Furthermore, bypassing model simulations may lead to significant time savings in complex models, for instance those found in population genetics. The Bayesian computation with empirical likelihood algorithm we develop in this paper also provides an evaluation of its own performance through an associated effective sample size. The method is illustrated using several examples, including estimation of standard distributions, time series, and population genetics models.
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