Convergence rate and concentration inequalities for Gibbs sampling in high dimension

Rates of Convergence for Gibbs Sampling for Variance Component Models

This paper analyzes the Gibbs sampler applied to a standard variance component model, and considers the question of how many iterations are required for convergence. It is proved that for K location parameters, with J observations each, the number of iterations required for convergence (for large K and J) is a constant times 1 + log K log J. This is one of the rst rigorous, a priori results abo...

Strong convergence for variational inequalities and equilibrium problems and representations

We introduce an implicit method for nding a common element of the set of solutions of systems of equilibrium problems and the set of common xed points of a sequence of nonexpansive mappings and a representation of nonexpansive mappings. Then we prove the strong convergence of the proposed implicit schemes to the unique solution of a variational inequality, which is the optimality condition for ...

Efficient Failure Rate Prediction for SRAM Cells via Gibbs Sampling

Bayesian inference of genetic parameters for reproductive traits in Sistani native cows using Gibbs sampling

This study was undertaken to estimate the genetic parameters for some reproduction traits in Sistani beef cattle. The data set consisted of 1489 records of number of insemination, calving, and insemination dates in different calving was used. Reproduction traits including calving interval (CI), gestation length (GL), days open (DO), calving to first service (CTFS), first service to conception (...

High performance parallel Gibbs sampling for IRT models

• Item Response Theory (IRT) yields models that describe a probabilistic relationship between correct responses on a set of items and a latent trait. • We can apply Gibbs sampling to the two-parameter normal model, however a large number of iterations is needed for the Markov chain to converge. • So, the algorithm is computationally intensive and demands significant execution time. • This fact ...