BayesiaLab
Spatio Temporal Multicomponent Optimal Learning State Estimation

Spatio-temporal Multicomponent Optimal Learning State Estimation

Presented at the 8th Annual BayesiaLab Conference on October 30, 2020.

Abstract

Geo-intelligence remote sensing platforms situated over spatially diverse areas are often tasked with geo-intelligence surveillance and adversarial monitoring for military organizations. Limited resources disallow continuous sampling of local areas at the same time, necessitating a need for smart sensing of diverse environments according to a rational evidence-based rule. Such algorithms should not only provide insight into which local region should be focused on, but should also facilitate decisions as to which environmental features should be measured over time once a local site has been selected. Multi-component optimal learning observational arrays are demonstrated using numerically simulated data of turbulent flow to show not only the feasibility of how individual observational platforms should be chosen in a Bayesian sense but also how goal state-directed sampling of complex systems or turbulent processes over local regions can be accomplished. A Bayesian amalgamation algorithm guides which observational arrays perform knowledge gradient policy based optimal learning to smartly sample observations in local regions. Machine learning and operations research algorithms function as data agnostic, Bayesian processors demonstrating how geo-intelligence information can be efficiently captured to help solve data-driven problems.

Presentation Video

About the Presenter

Nicholas Scott, Ph.D.
Principal Machine Learning Scientist
Open Innovation Center
Riverside Research
nscott@riversideresearch.org

Dr. Nicholas Scott is a modeling scientist and physical oceanographer and has been a member of the professional staff at Riverside Research in Dayton, OH, since October 2012. He investigates the applicability of traditional and non-traditional signal and image processing techniques to the extraction of information from remotely sensed imagery. This includes hyperspectral and multispectral imagery. His present work includes statistical modeling of geo-intelligence information, sensor array time series analysis of environmental data, and the application of pattern recognition techniques to turbulent flow imagery and numerically simulated data. He is also involved in the application of probabilistic graphical modeling algorithms for information fusion and statistical inference.

Previous Presentations

  • Bayesian Structural Field Analysis (Durham, 2019)

  • Bayesian Network Modeling of Imagery Features From Direct Numerically Simulated Turbulent Sediment-Laden Oscillatory Flow (Chicago, 2018)


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