Scalable Variational Inference for Bayesian Variable Selection in Regression, and Its Accuracy in Genetic Association Studies
The Bayesian approach to variable selection in regression is a powerful tool for tackling many scientific problems. Inference for variable selection models is usually implemented using Markov chain Monte Carlo (MCMC). Because MCMC can impose a high computational cost in studies with a large number o...
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| Huvudupphovsmän: | , |
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| Materialtyp: | Artigo |
| Språk: | engelska |
| Publicerad: |
2012
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| Länkar: | https://doi.org/10.1214/12-ba703 https://projecteuclid.org/journals/bayesian-analysis/volume-7/issue-1/Scalable-Variational-Inference-for-Bayesian-Variable-Selection-in-Regression-and/10.1214/12-BA703.pdf |
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