1.1k citations · 1.8k across the 24 of their papers we have counts for
24 papers
Analyzing trends in precipitation patterns using Hidden Markov model stochastic weather generators
Christopher J. Paciorek
We develop a flexible spline-based Bayesian hidden Markov model stochastic weather generator to statistically model daily precipitation over time by season at individual locations.…
A numerically stable online implementation and exploration of WAIC through variations of the predictive density, using NIMBLE
Joshua E. Hug, Christopher J. Paciorek
We go through the process of crafting a robust and numerically stable online algorithm for the computation of the Watanabe-Akaike information criteria (WAIC). We implement this alg…
Computational strategies and estimation performance with Bayesian semiparametric Item Response Theory models
Sally Paganin, Christopher J. Paciorek, Claudia Wehrhahn +3
Item response theory (IRT) models typically rely on a normality assumption for subject-specific latent traits, which is often unrealistic in practice. Semiparametric extensions bas…
Detected changes in precipitation extremes at their native scales derived from in situ measurements
Mark D. Risser, Christopher J. Paciorek, Travis A. O'Brien +2
The gridding of daily accumulated precipitation -- especially extremes -- from ground-based station observations is problematic due to the fractal nature of precipitation, and ther…
A probabilistic gridded product for daily precipitation extremes over the United States
Mark D. Risser, Christopher J. Paciorek, Michael F. Wehner +2
Gridded data products, for example interpolated daily measurements of precipitation from weather stations, are commonly used as a convenient substitute for direct observations beca…
Automatic adaptation of MCMC algorithms
Dao Nguyen, Perry de Valpine, Yves Atchade +3
Markov chain Monte Carlo (MCMC) methods are ubiquitous tools for simulation-based inference in many fields but designing and identifying good MCMC samplers is still an open questio…