Convergence rates and asymptotic standard errors for Markov chain Monte Carlo algorithms for Bayesian probit regression
By Vivekananda Roy, James P. Hobert. 2007
90 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/39fa32ce88cf3cb712ca263f7a3b02b97e1a8fe7
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In Artificial Intelligence
- James M. Flegal
- Murali Haran
- Galin L. Jones
- Peter Carbonetto
- Matthew Stephens
- Xiaoping Zhou
- Peter Donnelly
- Rémi Bardenet
In Statistics and Probability
- James M. Flegal
- Galin L. Jones
- Murali Haran
- Peter Carbonetto
- Matthew Stephens
- Xiaoping Zhou
- Dootika Vats
- Christina Knudson
Citations in the corpus
- Polygenic Modeling with Bayesian Sparse Linear Mixed Models (2012, background): “[72]) which are magnified here by the polygenic nature of the traits, and partly reflect robustness of linear models to model misspecification.”
- Batch means and spectral variance estimators in Markov chain Monte Carlo (2008, methodology): “Roy and Hobert (2007) implement RS for this example and we use their settings exactly except that we use only 50 regenerations.”
- Markov chain Monte Carlo: Can we trust the third significant figure? (2007, background): “For example, Hobert and Geyer (1998), Johnson and Jones (2008), Jones and Hobert (2004), Marchev and Hobert (2004), Mira and Tierney (2002), Robert (1995), Roberts and Polson (1994), Roberts and Rosenthal (1999), Rosenthal (1995, 1996), Roy and Hobert (2007) and Tierney (1994) examined Gibbs sampler”