Search
Now showing items 1-5 of 5
Implicit Sampling for Path Integral Control, Monte Carlo Localization, and SLAM
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Implicit sampling is a recently developed variationally enhanced sampling method that guides its samples to regions of high probability, so that each sample carries information. Implicit sampling may thus improve the ...
How Sampling Errors in Covariance Estimates Cause Bias in the Kalman Gain and Impact Ensemble Data Assimilation
Publisher: American Meteorological Society
A Theory for Why Even Simple Covariance Localization Is So Useful in Ensemble Data Assimilation
Publisher: American Meteorological Society
Implicit Particle Methods and Their Connection with Variational Data Assimilation
Publisher: American Meteorological Society
Abstract: he implicit particle filter is a sequential Monte Carlo method for data assimilation that guides the particles to the high-probability regions via a sequence of steps that includes minimizations. A new and more general ...
A Theory for Why Even Simple Covariance Localization Is So Useful in Ensemble Data Assimilation
Publisher: American Meteorological Society
CSV
RIS