2 citations · 3 across the 2 of their papers we have counts for
6 papers
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels
Marcus M. Noack, Harinarayan Krishnan, Mark D. Risser +1
A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications. This success is largely attributed to t…
Nonstationary Bayesian modeling for a large data set of derived surface temperature return values
Mark Risser
Heat waves resulting from prolonged extreme temperatures pose a significant risk to human health globally. Given the limitations of observations of extreme temperature, climate mod…
The effect of geographic sampling on evaluation of extreme precipitation in high resolution climate models
Mark D. Risser, Michael F. Wehner
Traditional approaches for comparing global climate models and observational data products typically fail to account for the geographic location of the underlying weather station d…
Bayesian inference for high-dimensional nonstationary Gaussian processes
Mark D. Risser, Daniel Turek
In spite of the diverse literature on nonstationary spatial modeling and approximate Gaussian process (GP) methods, there are no general approaches for conducting fully Bayesian in…
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…