Fast Bayesian Inference with Piecewise Deterministic Markov Processes
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Physics (MPPHS), MSc
Publicerad
2023
Författare
Hammar, Karl
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Piecewise 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.
Beskrivning
Ämne/nyckelord
Bayesian inference, Stochastic process, Piecewise Deterministic Markov Process, State estimation.