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Modeling spatio-temporal data using a Bayesian probabilistic cellular automata framework
(University of Missouri--Columbia, 2023)
Regularly gridded, or cellular, discrete-valued spatio-temporal data are common in many application areas. Such data can be considered from many perspectives, including deterministic or stochastic cellular automata, where ...
Alternative learning strategies for spatio-temporal processes of complex animal behavior
(University of Missouri--Columbia, 2020)
The estimation of spatio-temporal dynamics of animal behavior processes is complicated by nonlinear interactions. Alternative learning methods such as machine learning, deep learning, and reinforcement learning have proven ...
Methodologies for low-rank analysis and regionalization for multi-scale spatial datasets
(University of Missouri--Columbia, 2023)
[EMBARGOED UNTIL 5/1/2024] This dissertation comprises three chapters that focus on developing low-rank modeling and spatial aggregation techniques to overcome the computational and storage challenges associated with ...
A Bayesian approach to data-driven discovery of nonlinear dynamic equations
(University of Missouri--Columbia, 2022)
Dynamic equations parameterized by differential equations are used to represent a variety of real-world processes. The equations used to describe these processes are generally derived based on physical principles and a ...