Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition

522 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications (2011, methodology): “Geweke (2005) and Gamerman and Lopes (2006) cover more comprehensive theories and practices of the MCMC method.”
One and Done? Optimal Decisions From Very Few Samples (2014, background): “Similarly, Gamerman and Lopes (2006) estimate that to obtain a 95% confidence interval with sufficient precision requires 600, or even 3,746, independent samples.”
An Integrated Bayesian Model for DIF Analysis (2009, methodology): “The convergences of the Markov chains drawn from the Gibbs Sampler were tested using the R criteria of Gelman & Rubin (RGR) (see Gamerman & Lopes (2006) for details), calculated from 4 parallel chains after 10,000 iterations and different initial values.”
HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python (2013, methodology): “The inference algorithm, MCMC, requires the chains of the model to have properly converged.”