Bayesian versions of the classical one-way random effects model are widely used to analyze data. If the standard diffuse prior is adopted, there is a simple block Gibbs sampler that can be employed… (More)

In the classical biased sampling problem, we have k densities π1(·), …, πk (·), each known up to a normalizing constant, i.e. for l = 1, …, k, πl (·) = νl (·)/ml , where νl (·) is a known function… (More)

The differences of a phenotypic trait produced by a genotype in response to changes in the environment are referred to as phenotypic plasticity. Despite its importance in the maintenance of genetic… (More)

Journal of computational and graphical statistics…

2015

Importance sampling is a classical Monte Carlo technique in which a random sample from one probability density, π1, is used to estimate an expectation with respect to another, π. The importance… (More)

Abstract: For regression problems that involve many potential predictors, the Bayesian variable selection (BVS) method is a powerful tool, which associates each model with its posterior… (More)

Here, a q-dim binary vector γ = (γ1, . . . , γq) ∈ {0, 1}q =: Γ indicates a selected set of predictors, and βγ denotes the subvector of coefficients for the predictors selected by γ. Prior of the BVS… (More)

A Markov chain is geometrically ergodic if it converges to its invariant distribution at a geometric rate in total variation norm. We study geometric ergodicity of deterministic and random scan… (More)

Consider a parametric statistical model, P (dx|θ), and an improper prior distribution, ν(dθ), that together yield a (proper) formal posterior distribution, Q(dθ|x). The prior is calledstrongly… (More)

Markov chain Monte Carlo (MCMC) algorithms have greatly facilitated the popularity of Bayesian variable selection and model averaging in problems with high-dimensional covariates where enumeration of… (More)