Nicholas G. Polson

Learn More
Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables Nicholas G. Polson a , James G. Scott b & Jesse Windle c a Statistics and Econometrics , University of Chicago Booth School of Business , 1100 East 57th Street, Chicago , IL , 60637 b Statistics, University of Texas at Austin , 2110 Speedway, Stop B6500, Austin , TX , 78712 c(More)
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact Institute of(More)
We study the classic problem of choosing a prior distribution for a location parameter β = (β1, . . . , βp) as p grows large. First, we study the standard “global-local shrinkage” approach, based on scale mixtures of normals. Two theorems are presented which characterize certain desirable properties of shrinkage priors for sparse problems. Next, we review(More)
Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics for parameters and/or states as particles. State(More)
We present a new prior and corresponding algorithm for Bayesian analysis of the multinomial probit model. Our new approach places a prior directly on the identi"ed parameter space. The key is the speci"cation of a prior on the covariance matrix so that the (1,1) element if "xed at 1 and it is possible to draw from the posterior using standard distributions.(More)
Abstract. This paper develops particle learning (PL) methods for the estimation of general mixture models. The approach is distinguished from alternative particle filtering methods in two major ways. First, each iteration begins by resampling particles according to posterior predictive probability, leading to a more efficient set for propagation. Second,(More)