D. Woodard

发表

Convergence rate of Markov chain methods for genomic motif discovery

J. Rosenthal, D. Woodard, 2013, 1303.2814.

Travel time estimation for ambulances using Bayesian data augmentation

Shane G. Henderson, David S. Matteson, Bradford S. Westgate, 2013, 1312.1873.

Sufficient Conditions for Torpid Mixing of Parallel and Simulated Tempering

D. Woodard, M. Huber, S. Schmidler, 2009 .

Conditions for Rapid and Torpid Mixing of Parallel and Simulated Tempering on Multimodal Distributions

D. Woodard, M. Huber, S. Schmidler, 2009, 0906.2341.

Conditions for Torpid Mixing of Parallel and Simulated Tempering on Multimodal Distributions

D. Woodard, M. Huber, S. Schmidler, 2022 .

Dynamic pricing and matching in ride‐hailing platforms

D. Woodard, Chiwei Yan, Helin Zhu, 2018, Naval Research Logistics (NRL).

◾ Real-Time Identification of Performance Problems in Large Distributed Systems

Peter Bodik, Moises Goldszmidt, Dawn B. Woodard, 2016 .

Online Model-Based Clustering for Crisis Identification in Distributed Computing

M. Goldszmidt, D. Woodard, 2011 .

Predicting Bike Usage for New York City's Bike Sharing System

David B. Shmoys, Peter I. Frazier, Shane G. Henderson, 2015, AAAI Workshop: Computational Sustainability.

Spatial Inference of Nitrate Concentrations in Groundwater

R. Wolpert, D. Woodard, M. A. O'connell, 2010 .

Model-Based Image Segmentation via Monte Carlo EM , with Application to DCE-MRI

D. Woodard, C. Crainiceanu, 2013 .

LOWER BOUNDS ON THE CONVERGENCE RATES OF ADAPTIVE MCMC METHODS

D. Woodard, S. Schmidler, 2011 .

A Spatio-Temporal Mixture Model for Point Processes with Application to Ambulance Demand

Shane G. Henderson, David S. Matteson, Dawn B. Woodard, 2013 .

Latent factor regression models for grouped outcomes

Tanzy M. T. Love, D. Ruppert, D. Woodard, 2013, Biometrics.

A Pseudo-Marginal Perspective on the ABC Algorithm

Luke Bornn, Natesh S. Pillai, Dawn B. Woodard, 2014 .

Stationarity of generalized autoregressive moving average models

D. Matteson, D. Woodard, S. Henderson, 2011 .

The use of a single pseudo-sample in approximate Bayesian computation

N. Pillai, Aaron Smith, D. Woodard, 2016, Statistics and Computing.

Model-Based Clustering for Online Crisis Identification in Distributed Computing

Dawn B. Woodard, Moises Goldszmidt, M. Goldszmidt, 2009 .

Bayesian Modeling with S-PLUS® and the S+flexBayes Library

Dawn B. Woodard, Andrew Jack, D. Woodard, 2007 .

Predicting Travel Time Reliability using Mobile Phone GPS Data

Eric Horvitz, Dawn B. Woodard, Paul Koch, 2017 .

Forecasting emergency medical service call arrival rates

Mathew W. McLean, Shane G. Henderson, David S. Matteson, 2011, 1107.4919.

Dynamic pricing and matching in ride‐hailing platforms

Dawn B. Woodard, Helin Zhu, Chiwei Yan, 2019, Naval Research Logistics (NRL).

Fingerprinting the datacenter: automated classification of performance crises

Armando Fox, Moisés Goldszmidt, Peter Bodík, 2010, EuroSys '10.

One Pseudo-Sample is Enough in Approximate Bayesian Computation MCMC

Luke Bornn, Natesh S. Pillai, Dawn B. Woodard, 2014 .

The use of a single pseudo-sample in approximate Bayesian computation

Aaron Smith, Luke Bornn, Natesh S. Pillai, 2014, Stat. Comput..

A Spatio-Temporal Point Process Model for Ambulance Demand

Shane G. Henderson, David S. Matteson, Dawn B. Woodard, 2014, 1401.5547.

European Journal of Operational Research Large-network Travel Time Distribution Estimation for Ambulances

Shane G. Henderson, David S. Matteson, Bradford S. Westgate, 2022 .

Performance assessment for radiologists interpreting screening mammography

W E Barlow, A. Gelfand, D. Woodard, 2007, Statistics in medicine.

Hierarchical Adaptive Regression Kernels for Regression With Functional Predictors

David Ruppert, Ciprian Crainiceanu, Dawn B Woodard, 2013, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.