Particle Filter Tutorial 2: Algorithm Derivation

#python #statistics #probability #scipy #scientificcomputing #stats #bayesian #normaldistribution #statisticsvideolectures #controltheory #controlengineering #mechatronics #robotics #machinelearning #mechanicalengineering #electricalengineering #datascientist #dynamicalsystems #dynamics #machinelearning #robotics It takes a significant amount of time and energy to create these free video tutorials. You can support my efforts in this way: - Buy me a Coffee: https://www.buymeacoffee.com/AleksandarHaber - PayPal: https://www.paypal.me/AleksandarHaber - Patreon: https://www.patreon.com/user?u=32080176&fan_landing=true - You Can also press the Thanks YouTube Dollar button In this estimation, control theory, machine learning, signal processing, and data science tutorial, we provide a clear and concise explanation of a particle filter algorithm. We focus on the problem of using the particle filter algorithm for state estimation of dynamical systems. Besides providing a detailed explanation of particle filters, we also explain how to implement the particle filter algorithm from scratch in Python. Part 2. In this tutorial, we derive the particle filter algorithm from scratch. We derive the bootstrap algorithm which is also known as the Sequential Importance Resampling (SIR) algorithm. These tutorials are specially designed for students who are not experts in statistics and who are not experts in control theory. We thoroughly explain all the used statistics concepts. Nowadays, there is a trend not to thoroughly study theoretical concepts and quickly jump to Python scripts implementing algorithms, without first properly understanding the theoretical background of algorithms. If you follow such an approach, you will never be able to understand particle filters, and you will not even be able to understand the Python implementation of particle filters. To properly understand particle filters, you need to first properly understand important concepts from dynamical system theory, probability theory, and statistics. These concepts are thoroughly explained in this tutorial. Consequently, reserve some time and stay focused while reading this tutorial. Do not immediately jump to the second or third parts without properly understanding the material presented in this tutorial.