This is a recurring meeting, therefore please register just once for all 4 sessions:
https://zoom.us/meeting/register/tJcldOuppjIpGNNliClqvaXYFEJG7p2397Q1




Timetable
Monday, 15 May, 10:00-12:00
Tuesday, 16 May, 10:00-12:00
Wednesday, 17 May, 10:00-12:00
Thursday, 18 May, 10:00-12:00


Venue (for in-person participants)
Stasi Lecture Hall (Leonardo da Vinci Building, first floor)



Abstract

Nonlinear filtering is one of the main approaches to tackle many problems involving with fields such as financial mathematics, engineering sciences, fluid dynamics, physics, etc. Unfortunately, it is impossible to obtain analytical solutions for most of the stochastic nonlinear filtering models associated with the practical problems arising in the abovementioned fields, excepting a small number of simple cases. Particle filtering plays an important role to solve such stochastic nonlinear filtering models numerically. In this short course, we are going to provide an introductory knowledge on nonlinear filtering theory and associated particle filtering approach. First, we will define the nonlinear filtering problem and then, step by step, we will discuss the necessary tools (such as conditional expectation, Bayes formula, normalized and unnormalized conditional measures, Novikov’s condition, Girsanov transformation, Kallianpur-Striebel formula, etc.) to derive the measure-valued filtering equations known as Zakai equation and FKK equation. Then we will discuss a couple of particle filtering algorithms such as Monte-Carlo method, branching particle method, etc.