Introduction to Automated Particle Analysis

Introduction to Particle Physics

Introduction to Automated Planning

Foreword These are the lecture notes of the AI planning course at the Albert-Ludwigs-University Freiburg in summer term 2005, based on earlier notes for the course in winter 2002/2003 and in summer 2004. I would like to thank all the students who have participated in the planning course and given comments, pointed out errors, and suggested other improvements, including Slawomir Grzonka,

Introduction to Subatomic-particle Spectrometers *

An introductory review, suitable for the beginning student of high-energy physics or professionals from other fields who may desire familiarity with subatomic-particle detection techniques. Subatomic-particle fundamentals and the basics of particle interactions with matter are summarized, after which we review particle detectors. We conclude with two examples that illustrate the variety of suba...

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 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...