Pages that link to "Item:Q4455367"

The following pages link to On the applicability of regenerative simulation in Markov chain Monte Carlo (Q4455367):

Displaying 39 items.

• Markov chain Monte Carlo estimation of quantiles (Q485905) (← links)
• Geometric ergodicity of random scan Gibbs samplers for hierarchical one-way random effects models (Q495387) (← links)
• A short history of Markov chain Monte Carlo: Subjective recollections from incomplete data (Q635417) (← links)
• Variance bounding Markov chains (Q930683) (← links)
• Exact simulation for discrete time spin systems and unilateral fields (Q937163) (← links)
• Consistent estimation of the accuracy of importance sampling using regenerative simulation (Q951212) (← links)
• Approximate regenerative-block bootstrap for Markov chains (Q1023604) (← links)
• Permuted derivative and importance-sampling estimators for regenerative simulations. (Q1429938) (← links)
• Honest exploration of intractable probability distributions via Markov chain Monte Carlo. (Q1431211) (← links)
• Weighted batch means estimators in Markov chain Monte Carlo (Q1616318) (← links)
• A mixture representation of \(\pi\) with applications in Markov chain Monte Carlo and perfect sampling. (Q1879910) (← links)
• Exact sampling for intractable probability distributions via a Bernoulli factory (Q1950803) (← links)
• Convergence rates for MCMC algorithms for a robust Bayesian binary regression model (Q1950912) (← links)
• Gibbs sampling for a Bayesian hierarchical general linear model (Q1952054) (← links)
• Convergence rates of two-component MCMC samplers (Q2136999) (← links)
• Regeneration-enriched Markov processes with application to Monte Carlo (Q2240830) (← links)
• Geometric ergodicity of Gibbs samplers for Bayesian general linear mixed models with proper priors (Q2341879) (← links)
• Batch means and spectral variance estimators in Markov chain Monte Carlo (Q2380096) (← links)
• An MCMC approach to empirical Bayes inference and Bayesian sensitivity analysis via empirical processes (Q2413604) (← links)
• A theoretical comparison of the data augmentation, marginal augmentation and PX-DA algorithms (Q2426614) (← links)
• Estimation of integrals with respect to infinite measures using regenerative sequences (Q2794730) (← links)
• Geometric Ergodicity and Scanning Strategies for Two-Component Gibbs Samplers (Q2794784) (← links)
• Layer Sampling (Q2809586) (← links)
• Regenerative Markov Chain Monte Carlo for Any Distribution (Q3168385) (← links)
• Using a Markov Chain to Construct a Tractable Approximation of an Intractable Probability Distribution (Q3411057) (← links)
• The Markov chain Monte Carlo revolution (Q3623558) (← links)
• Regenerative Markov Chain Importance Sampling (Q4976578) (← links)
• Bayesian networks: regenerative Gibbs samplings (Q5055233) (← links)
• Assessing and Visualizing Simultaneous Simulation Error (Q5066389) (← links)
• Assessing the significance of peptide spectrum match scores (Q5111811) (← links)
• Ascent-Based Monte Carlo Expectation– Maximization (Q5313587) (← links)
• Nonasymptotic Bounds on the Mean Square Error for MCMC Estimates via Renewal Techniques (Q5326129) (← links)
• Bayesian estimation in Kibble's bivariate gamma distribution (Q5486558) (← links)
• The Number of MCMC Draws Needed to Compute Bayesian Credible Bounds (Q5884453) (← links)
• Component-wise Markov chain Monte Carlo: uniform and geometric ergodicity under mixing and composition (Q5965030) (← links)
• Strong invariance principles for ergodic Markov processes (Q6200877) (← links)
• Multivariate strong invariance principles in Markov chain Monte Carlo (Q6597254) (← links)
• Analyzing Markov chain Monte Carlo output (Q6601094) (← links)
• A convergence diagnostic for Bayesian clustering (Q6602121) (← links)