3 papers
stat.ML2018
Deep Echo State Networks with Uncertainty Quantification for Spatio-Temporal Forecasting
Patrick L. McDermott, Christopher K. Wikle
Long-lead forecasting for spatio-temporal systems can often entail complex nonlinear dynamics that are difficult to specify it a priori. Current statistical methodologies for model…
stat.ML2017
An Ensemble Quadratic Echo State Network for Nonlinear Spatio-Temporal Forecasting
Patrick L. McDermott, Christopher K. Wikle
Spatio-temporal data and processes are prevalent across a wide variety of scientific disciplines. These processes are often characterized by nonlinear time dynamics that include in…
stat.CO2016
Methods for Bayesian Variable Selection with Binary Response Data using the EM Algorithm
Patrick McDermott, John Snyder, Rebecca Willison
High-dimensional Bayesian variable selection problems are often solved using computationally expensive Markov Chain Montle Carlo (MCMC) techniques. Recently, a Bayesian variable se…