Erik Lindström: Iterated filtering, historical background and future developments

Maximum Likelihood estimation for partially observed Markov process models is a non-trivial problem, as the likelihood function typically is unknown. Iterated Filtering is a simple, yet very general algorithm for computing the Maximum Likelihood estimate. The algorithm is 'plug and play' in the sense that it can be used with rudimentary statistical knowledge. The purpose of this talk is to discuss the algorithm, pointing out practical limitations, and suggest extensions and/or modifications that will improve the robustness and/or performance of the algorithm. We will also discuss the connection between the Iterated Filtering algorithm, and algorithms commonly used in engineering (system identification, signal processing etc.), illustrating that a similar algorithm has been known for several decades.

(OBS!!!) The time of the talk is one hour earlier than usual time.