High-resolution computation of isotopic processes in northern California using a local climate model

EXTRACT (SEE PDF FOR FULL ABSTRACT): We describe a coupled local climate/isotope model that can calculate Rayleigh-type processes of distillation and fractionation of hydrogen isotopes along individual air mass flowlines in the western United States.This climate model is an extension of that detailed earlier by Craig and Stamm (1990). ... Volumetric effects of evapotranspiration (ET) are included. The model allows sensitivity studies of the influence of ET recycling.

Bayesian two-sample tests

In this paper, we present two classes of Bayesian approaches to the two-sample problem. Our first class of methods extends the Bayesian t-test to include all parametric models in the exponential family and their conjugate priors. Our second class of methods uses Dirichlet process mixtures (DPM) of such conjugate-exponential distributions as flexible nonparametric priors over the unknown distributions.

Nonparametric Bayesian sparse factor models with application to gene expression modeling

A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data $\mathbf{Y}$ is modeled as a linear superposition, $\mathbf{G}$, of a potentially infinite number of hidden factors, $\mathbf{X}$. The Indian Buffet Process (IBP) is used as a prior on $\mathbf{G}$ to incorporate sparsity and to allow the number of latent features to be inferred. The model's utility for modeling gene expression data is investigated using randomly generated data sets based on a known sparse connectivity matrix for E. Coli, and on three biological data sets of increasing complexity.