Monte Carlo Methods in Bayesian Computation

By Ming-Hui Chen, Q. Shao, Joseph G. Ibrahim. 2000

367 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e108c3f3efc4886b20cc771e60756e72f413d567

How researchers see this paper

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

In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Model comparison of nonlinear structural equation models with fixed covariates (2003, methodology): “Finally in the future, it is interesting to investigate the extensions of the path sampling method to densities with different dimensions as in Chen, Shao and Ibrahim (2000) , and to apply it to other more complicated psychometric models.”
Hierarchical Bayes Modeling of Survey-Weighted Small Area Proportions (2014, neutral): “For example, small area estimation methods are used: in the Census Bureau’s Small Area Income and Poverty Estimates (SAIPE) program to estimate poverty rates for states, counties, and school districts (Citro and Kalton, 2000; Maples and Bell, 2005); with data from the National Survey on Drug Use and”
A Bayesian Random Effects Discrete-Choice Model for Resource Selection: Population-Level Selection Inference (2006, methodology): “Our programming approach was based on hierarchical centering, which improves the efficiency of estimation (Chen et al. 2000).”
Why is it difficult to accurately predict the COVID-19 epidemic? (2020, positive): “Commonly used MCMC algorithms include the Metropolis-Hastings algorithm and Random-Walk Metropolis-Hastings algorithms (Chen et al., 2000).”
Testing hypotheses via a mixture estimation model (2014, methodology): “X The computation of marginal likelihoods is difficult in most settings (Chen et al., 2000; Marin and Robert, 2011) with further controversy about which solution to adopt (Newton and Raftery, 1994; Neal, 1994; Green, 1995; Chib, 1995; Neal, 1999; Skilling, 2006; Steele et al., 2006; Chopin and…”
A Bayesian Approach to Imposing Curvature on Distance Functions (2005, methodology): “Details of the M-H algorithm can be found in, for example, Chen, Shao and Ibrahim (2000, p.23-24) .”
The expected demise of the Bayes factor (2015, background): “From a computational perspective, let me recall there is no universally acknowledged approach to compute marginal likelihoods and Bayes factors (Chen et al., 2000; Marin and Robert, 2011), while some approaches are notoriously misleading (Newton and Raftery, 1994).”
Markov chain Monte Carlo: Can we trust the third significant figure? (2007, neutral): “ar X iv :m at h/ 07 03 74 6v 5 [ m at h. ST ] 9 S ep 2 00 Key words and phrases: Convergence diagnostic, Markov chain, Monte Carlo, standard errors.”
A novel genealogical approach to neutral biodiversity theory (2004, methodology): “We propose to use a Bayesian Markov Chain Monte Carlo approach ( Chen et al. 2000 ).”
The use of auxiliary variables in capture-recapture modelling: An overview (2002, methodology): “This is due to the development of eý cient computer-based sampling methods to estimate posterior distributions using Markov Chain Monte Carlo (MCMC) and the BUGS software (Gilks et al., 1996; Spiegelhalter et al., 1999; Chen et al., 2000).”
Fast and accurate calculations for first-passage times in Wiener diffusion models (2009, methodology): “In particular, most Bayesian estimation methods (e.g., Chen et al. 2000) rely226 heavily on the use of the exact probability density functions rather than χ2 statistics,227 for instance, and so can benefit from this approach.”
Bayesian correlation estimation (2004, methodology): “The awkward manner in which r ij is embedded in the likelihood complicates posterior simulation, leading us to use a Metropolis–Hastings algorithm to update one coefficient r ij at a time (Barnard et al., 2000); see for example Chib & Greenberg (1995) for a review of the Metropolis–Hastings…”
The Mixed Logit model: The state of practice (2003, background): “Brownstone (2001) provides a useful overview as do Chen, Shao, and Ibrahim (2000) , Geweke (1999) and Train (2001).”
Properties of nested sampling (2008, methodology): “We describe below the solutions used in the subsequent comparison with nested sampling, but we do not pretend at an exhaustive coverage of those techniques, see Chen et al. (2000) or Han & Carlin (2001) for a deeper coverage, nor at using the most efficient approach, see Meng & Schilling (2002).”