5 citations · 6 across the 4 of their papers we have counts for
5 papers
Approximating the marginal likelihood using copula
David J. Nott, Robert J. Kohn, Mark Fielding
Model selection is an important activity in modern data analysis and the conventional Bayesian approach to this problem involves calculation of marginal likelihoods for different m…
Adaptive Independent Metropolis-Hastings by Fast Estimation of Mixtures of Normals
P. Giordani, R. Kohn
We construct an adaptive independent Metropolis-Hastings sampler that uses a mixture of normals as a proposal distribution. To take full advantage of the potential of adaptive samp…
Locally Adaptive Nonparametric Binary Regression
Sally Wood, Robert Kohn, Remy Cottet +2
A nonparametric and locally adaptive Bayesian estimator is proposed for estimating a binary regression. Flexibility is obtained by modeling the binary regression as a mixture of pr…
Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models
Remy Cottet, Robert Kohn, David Nott
We express the mean and variance terms in a double exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predi…
Bayesian Covariance Matrix Estimation using a Mixture of Decomposable Graphical Models
Helen Armstrong, Christopher K. Carter, Kevin F. Wong +1
A Bayesian approach is used to estimate the covariance matrix of Gaussian data. Ideas from Gaussian graphical models and model selection are used to construct a prior for the covar…