@inbook{4ea9bd2a9da24e7f9fd4c3f7ffdb85a9,
title = "A practical example for the non-linear bayesian filtering of model parameters",
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.",
keywords = "Model parameters, Non-linear Bayesian filtering, Particle-based filters, Sequential Monte Carlo, Sequential importance sampling, Time-dependent model",
author = "Matthieu Bult{\'e} and Jonas Latz and Elisabeth Ullmann",
note = "Publisher Copyright: {\textcopyright} National Technology & Engineering Solutions of Sandia, and The Editor(s), under exclusive license to Springer Nature Switzerland AG 2020.",
year = "2020",
doi = "10.1007/978-3-030-48721-8_11",
language = "English",
series = "Lecture Notes in Computational Science and Engineering",
publisher = "Springer",
pages = "241--272",
booktitle = "Lecture Notes in Computational Science and Engineering",
}