A practical example for the non-linear bayesian filtering of model parameters

Matthieu Bulté, Jonas Latz, Elisabeth Ullmann

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations

Abstract

In this tutorial we consider the non-linear Bayesian filtering of static parameters in a time-dependent model. We outline the theoretical background and discuss appropriate solvers. We focus on particle-based filters and present Sequential Importance Sampling (SIS) and Sequential Monte Carlo (SMC). Throughout the paper we illustrate the concepts and techniques with a practical example using real-world data. The task is to estimate the gravitational acceleration of the Earth g by using observations collected from a simple pendulum. Importantly, the particle filters enable the adaptive updating of the estimate for g as new observations become available. For tutorial purposes we provide the data set and a Python implementation of the particle filters.

Original languageEnglish
Title of host publicationLecture Notes in Computational Science and Engineering
PublisherSpringer
Pages241-272
Number of pages32
DOIs
StatePublished - 2020

Publication series

NameLecture Notes in Computational Science and Engineering
Volume137
ISSN (Print)1439-7358
ISSN (Electronic)2197-7100

Keywords

  • Model parameters
  • Non-linear Bayesian filtering
  • Particle-based filters
  • Sequential Monte Carlo
  • Sequential importance sampling
  • Time-dependent model

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