4 papers
Bayesian Nonparametric Approaches to Ordinal Drought Modeling in the United States
Mostafa Shams, Robert Erhardt, Staci Hepler
Data observed over space and time can exhibit dependence across both dimensions, and datasets can grow very large in both the number of locations and the number of time periods. Th…
Three Case Studies of Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Data
Mostafa Shams, Robert Erhardt, Staci Hepler
High dimensional spatio-temporal models can quickly run up against computational limitations. Recursive or multi-stage Bayesian algorithms are one way to address this computational…
The Impact of a Gridded Streamflow Measure on Drought Variation in the Conterminous United States
Rob Erhardt, Courtney Di Vittorio, Staci Hepler +3
Models for droughts draw on a wide range of meteorological and hydrological inputs. Stakeholders classify droughts according to different purposes and priorities, and accordingly r…
Two-stage MCMC for Fast Bayesian Inference of Large Spatio-temporal Ordinal Data, with Application to US Drought
Staci Hepler, Rob Erhardt
High dimensional space-time data pose known computational challenges when fitting spatio-temporal models. Such data show dependence across several dimensions of space as well as in…