An Introduction to Box Particle Filtering [Lecture Notes]

Introduction to Box Particle Filtering

Resulting from the synergy between the sequential Monte Carlo (SMC) method [1] and interval analysis [2], box particle filtering is a recently emerged approach aimed at solving a general class of nonlinear filtering problems. This approach is particularly appealing in practical situations involving imprecise stochastic measurements that result in very broad posterior densities. It relies on the...

Introduction to the Box Particle Filtering

Resulting from the synergy between the sequential Monte Carlo (SMC) method [1] and interval analysis [2], the box particle filtering is a recently emerged approach aimed at solving a general class of nonlinear filtering problems. This approach is particularly appealing in practical situations involving imprecise stochastic measurements, thus resulting in very broad posterior densities. It relie...

An Introduction to Particle Filtering

This report introduces the ideas behind particle filters, looking at the Kalman filter and the SIS and SIR filters to learn about the latent state of state space models. It then introduces particle MCMC as a way of learning about the parameters behind these models. Finally, the SIR filter and particle MCMC algorithms are applied to reaction networks, in particular the Lotka Volterra model.

Introduction to Probability: Lecture Notes

A probabilistic model of an experiment is defined by a probability space consisting of a set (sample space Ω) of sample points or outcomes (exhaustive collection of elementary outcomes of the experiment) and a probability law P which assigns to each event in (subset of) Ω a probability satisfying three axioms: (1) nonnegativity, i.e., all probabilities are nonnegative; (2) additivity, i.e., the...

Introduction to Macroeconomics Lecture Notes