Neural Networks for Parameter Estimation in Intractable Models

no code implementations • 29 Jul 2021 • Amanda Lenzi, Julie Bessac, Johann Rudi, Michael L. Stein

We propose to use deep learning to estimate parameters in statistical models when standard likelihood estimation methods are computationally infeasible.

Linear-Cost Covariance Functions for Gaussian Random Fields

1 code implementation • 16 Nov 2017 • Jie Chen, Michael L. Stein

An essential ingredient of GRF is the covariance function that characterizes the joint Gaussian distribution of the field.

Methodology Numerical Analysis

Locally stationary spatio-temporal interpolation of Argo profiling float data

1 code implementation • 1 Nov 2017 • Mikael Kuusela, Michael L. Stein

Argo floats measure sea water temperature and salinity in the upper 2, 000 m of the global ocean.

Applications Atmospheric and Oceanic Physics Methodology

Estimating deformations of isotropic Gaussian random fields on the plane

no code implementations • 4 Apr 2008 • Ethan B. Anderes, Michael L. Stein

This paper presents a new approach to the estimation of the deformation of an isotropic Gaussian random field on $\mathbb{R}^2$ based on dense observations of a single realization of the deformed random field.

Statistics Theory Statistics Theory 62M30, 62M40 (Primary) 60G60 (Secondary)