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Found 6 papers, 0 papers with code
elhmc: An R Package for Hamiltonian Monte Carlo Sampling in Bayesian Empirical Likelihood
In this article, we describe a {\tt R} package for sampling from an empirical likelihood-based posterior using a Hamiltonian Monte Carlo method.
A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Bayesian Model Selection
Furthermore, we discuss Bayesian model selection using empirical likelihood and extend our two-step Metropolis Hastings algorithm to a reversible jump Markov chain Monte Carlo procedure to sample from the resulting posterior.
On a Variational Approximation based Empirical Likelihood ABC Method
The expected log-likelihood is then estimated by an empirical likelihood where the only inputs required are a choice of summary statistic, it's observed value, and the ability to simulate the chosen summary statistics for any parameter value under the model.
Maximum Likelihood under constraints: Degeneracies and Random Critical Points
In the Bayesian setting, we rigorously establish the posterior consistency of procedures based on these ideas, where instead of a parametric likelihood, an empirical likelihood is used to define the posterior distribution.
An easy-to-use empirical likelihood ABC method
Many scientifically well-motivated statistical models in natural, engineering and environmental sciences are specified through a generative process, but in some cases it may not be possible to write down a likelihood for these models analytically.
Qualitative inequalities for squared partial correlations of a Gaussian random vector
Rules for comparing degree of association among the vertices of such Gaussian graphical models are also developed.
