  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.
