Fast Bayesian Inference with Piecewise Deterministic Markov Processes

dc.contributor.authorHammar, Karl
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.examinerSchauer, Moritz
dc.contributor.supervisorAndersson, Adam
dc.contributor.supervisorSvedung Wettervik, Benjamin
dc.date.accessioned2023-06-27T08:14:57Z
dc.date.available2023-06-27T08:14:57Z
dc.date.issued2023
dc.date.submitted2023
dc.description.abstractPiecewise Deterministic Markov Processes (PDMPs) present a recent class of samplers for Bayesian inference. In this thesis, PDMP samplers are employed to sample state and latent parameters of an adversarial missile, described by an SDE. An approximate method for fast sampling is developed for this problem, and the performance of two different PDMP samplers, the Zig-Zag sampler, and the bouncy particle sampler, are compared. We find that the approximations needed for the methods to be competitive have a small impact on accuracy and that the method has the potential to be useful in real-world applications. Additionally, an approach for sampling from target models which may experience discontinuous jumps is developed. Using a particular trajectory realization of one such model we show that the method works as expected. This bears importance for the sampling of parameters related to maneuvering target types, where jump dynamics are relevant for target modeling.
dc.identifier.coursecodeMVEX03
dc.identifier.urihttp://hdl.handle.net/20.500.12380/306418
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectBayesian inference, Stochastic process, Piecewise Deterministic Markov Process, State estimation.
dc.titleFast Bayesian Inference with Piecewise Deterministic Markov Processes
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmePhysics (MPPHS), MSc

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