91 citations · 330 across the 8 of their papers we have counts for
8 papers
Reversible Jump MCMC With No Regrets: Bayesian Variable Selection Using Mixtures of Mutually Singular Distributions
Don van den Bergh, Merlise A. Clyde, Adrian E. Raftery +1
Bayesian variable selection requires sampling from a posterior distribution that combines discrete model indicators with continuously varying parameters, a challenge often addresse…
Mixtures of g-priors in Generalized Linear Models
Yingbo Li, Merlise A. Clyde
Mixtures of Zellner's g-priors have been studied extensively in linear models and have been shown to have numerous desirable properties for Bayesian variable selection and model av…
Stochastic expansions using continuous dictionaries: Lévy adaptive regression kernels
Robert L. Wolpert, Merlise A. Clyde, Chong Tu
This article describes a new class of prior distributions for nonparametric function estimation. The unknown function is modeled as a limit of weighted sums of kernels or generator…
Bayesian Methods for Analysis and Adaptive Scheduling of Exoplanet Observations
Thomas J. Loredo, James O. Berger, David F. Chernoff +2
We describe work in progress by a collaboration of astronomers and statisticians developing a suite of Bayesian data analysis tools for extrasolar planet (exoplanet) detection, pla…
Bayesian nonparametric models for peak identification in MALDI-TOF mass spectroscopy
Leanna L. House, Merlise A. Clyde, Robert L. Wolpert
We present a novel nonparametric Bayesian approach based on Lévy Adaptive Regression Kernels (LARK) to model spectral data arising from MALDI-TOF (Matrix Assisted Laser Desorption…
Generalized Beta Mixtures of Gaussians
Artin Armagan, David B. Dunson, Merlise Clyde
In recent years, a rich variety of shrinkage priors have been proposed that have great promise in addressing massive regression problems. In general, these new priors can be expres…