Pages that link to "Item:Q2477054"
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The following pages link to Stability of the Gibbs sampler for Bayesian hierarchical models (Q2477054):
Displaying 19 items.
- Variable transformation to obtain geometric ergodicity in the random-walk Metropolis algorithm (Q139529) (← links)
- A general framework for the parametrization of hierarchical models (Q449750) (← links)
- Geometric ergodicity of random scan Gibbs samplers for hierarchical one-way random effects models (Q495387) (← links)
- A spectral analytic comparison of trace-class data augmentation algorithms and their sandwich variants (Q661172) (← links)
- Convergence analysis of the Gibbs sampler for Bayesian general linear mixed models with improper priors (Q741808) (← links)
- Particle learning and smoothing (Q903317) (← links)
- A comparison of centring parameterisations of Gaussian process-based models for Bayesian computation using MCMC (Q1703834) (← links)
- Adaptive Gibbs samplers and related MCMC methods (Q1948684) (← links)
- Gibbs sampling for a Bayesian hierarchical general linear model (Q1952054) (← links)
- Geometric ergodicity of Gibbs samplers for Bayesian general linear mixed models with proper priors (Q2341879) (← links)
- Nonasymptotic bounds on the estimation error of MCMC algorithms (Q2435233) (← links)
- Analysis of the Gibbs Sampler for Hierarchical Inverse Problems (Q2938456) (← links)
- Data transforming augmentation for heteroscedastic models (Q5066014) (← links)
- Convergence of Conditional Metropolis-Hastings Samplers (Q5169501) (← links)
- The Efficiency of Next-Generation Gibbs-Type Samplers: An Illustration Using a Hierarchical Model in Cosmology (Q5283095) (← links)
- Component-wise Markov chain Monte Carlo: uniform and geometric ergodicity under mixing and composition (Q5965030) (← links)
- Non-centered parametric variational Bayes’ approach for hierarchical inverse problems of partial differential equations (Q6129005) (← links)
- Scalable Bayesian computation for crossed and nested hierarchical models (Q6184925) (← links)
- Density regression and uncertainty quantification with Bayesian deep noise neural networks (Q6548857) (← links)
