Skip to main content
main-content

Tipp

Weitere Kapitel dieses Buchs durch Wischen aufrufen

2019 | OriginalPaper | Buchkapitel

Sequential Bayesian Inference for Dynamical Systems Using the Finite Volume Method

verfasst von : Colin Fox, Richard A. Norton, Malcolm E. K. Morrison, Timothy C. A. Molteno

Erschienen in: 2017 MATRIX Annals

Verlag: Springer International Publishing

share
TEILEN

Optimal Bayesian sequential inference, or filtering, for the state of a deterministic dynamical system requires simulation of the Frobenius-Perron operator, that can be formulated as the solution of an initial value problem in the continuity equation on filtering distributions. For low-dimensional, smooth systems the finite-volume method is an effective solver that conserves probability and gives estimates that converge to the optimal continuous-time values. A Courant–Friedrichs–Lewy condition assures that intermediate discretized solutions remain positive density functions. We demonstrate this finite-volume filter (FVF) in a simulated example of filtering for the state of a pendulum, including a case where rank-deficient observations lead to multi-modal probability distributions.

Metadaten
Titel
Sequential Bayesian Inference for Dynamical Systems Using the Finite Volume Method
verfasst von
Colin Fox
Richard A. Norton
Malcolm E. K. Morrison
Timothy C. A. Molteno
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-04161-8_2

Premium Partner