#python #statistics #probability #scipy #scientificcomputing #stats #bayesian #normaldistribution #statisticsvideolectures #controltheory #controlengineering #mechatronics #robotics #machinelearning #mechanicalengineering #electricalengineering #datascientist #dynamicalsystems #dynamics #machinelearning
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: www.buymeacoffee.com/Aleksand...
- PayPal: www.paypal.me/AleksandarHaber
- Patreon: www.patreon.com/user?u=320801...
- You Can also press the Thanks KZitem Dollar button
In this Python, statistics, estimation, and mathematics tutorial, we introduce the concept of importance sampling. The importance sampling method is a Monte Carlo method for approximately computing expectations and integrals of functions of random variables. The importance sampling method is extensively used in computational physics, Bayesian estimation, machine learning, and particle filters for state estimation of dynamical systems. For us, the most interesting application is particle filters for state estimation of dynamical systems. This topic will be covered in our future tutorials. For the time being it is of paramount importance to properly explain the importance sampling method. Besides explaining the importance sampling method, in this tutorial, we also explain how to implement the importance sampling method in Python and its SciPy library. This is an intro tutorial on particle filters.
Негізгі бет Ғылым және технология Importance Sampling - Detailed Tutorial with Python Implementation - Intro to Particle Filters
Пікірлер: 3