2 citations · 2 across the 3 of their papers we have counts for
6 papers
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…
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
Patrick L. McDermott, Christopher K. Wikle
Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential…
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…
A Hierarchical Spatio-Temporal Analog Forecasting Model for Count Data
Patrick L. McDermott, Christopher K. Wikle, Joshua Millspaugh
1. Analog forecasting has been successful at producing robust forecasts for a variety of ecological and physical processes. Analog forecasting is a mechanism-free nonlinear method…
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…
A Model-Based Approach for Analog Spatio-Temporal Dynamic Forecasting
Patrick L. McDermott, Christopher K. Wikle
Analog forecasting has been applied in a variety of fields for predicting future states of complex nonlinear systems that require flexible forecasting methods. Past analog methods…