A Particle Filter Method of Inference for Stochastic Differential Equations
Abstract
Stochastic Differential Equations (SDE) serve as an extremely useful modelling tool
in areas including ecology, finance, population dynamics, and physics. Yet, parameter
inference This thesis explores this latter approach. Specifically, we use a particle filter
tailored to SDE models and consider various methods for approximating the gradient
and hessian of the parameter log-posterior. Empirical results for several SDE models are
presented.
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Cite this version of the work
Pranav Subramani
(2022).
A Particle Filter Method of Inference for Stochastic Differential Equations. UWSpace.
http://hdl.handle.net/10012/18342
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