The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Bayesian auxiliary variable models for binary and multinomial regression
Open accessAbstract
In this paper we discuss auxiliary variable approaches to Bayesian binary and multinomial regression. These approaches are ideally suited to automated Markov chain Monte Carlo simulation. In the first part we describe a simple technique using joint updating that improves the performance of the conventional probit regression algorithm. In the second part we discuss auxiliary variable methods for inference in Bayesian logistic regression, including covariate set uncertainty. Finally, we show how the logistic method is easily extended to multinomial regression models. All of the algorithms are fully automatic with no user set parameters and no necessary Metropolis-Hastings accept/reject steps.
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Cites
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Cited by
Rue, H., Martino, S., & Chopin, N. (2009) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2008.00700.x · Cited by 5,279
Riemann manifold langevin and hamiltonian monte carlo methods
Girolami, M., & Calderhead, B. (2011) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2010.00765.x · Cited by 1,473
Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
Polson, N. G., Scott, J. G., & Windle, J. (2013) · Journal of the American Statistical Association · doi:10.1080/01621459.2013.829001 · Cited by 928
Simultaneous Analysis of All SNPs in Genome-Wide and Re-Sequencing Association Studies
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LOGICOIL—multi-state prediction of coiled-coil oligomeric state
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Metadata
- Authors
- Held, L., & Holmes, C. C.
- Venue
- Bayesian Analysis
- Publisher
- Institute of Mathematical Statistics
- Published
- 2006-03-01
- Volume
- 1
- Issue
- 1
- Pages
- 145-168
- ISSN
- 1931-6690 (ISSN-L)
- 1936-0975
- OpenAlex
- W2051817458
- Cited by
- 434
- References
- 18
- FWCI
- 15.25
- Type
- journal article
- Language
- en
- Open access
- diamond
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