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Jimmy Olsson: Statistical inference and learning in general state-space models

Inauguration lecture

Time: Wed 2022-04-06 15.15 - 16.00

Location: F3

Lecturer: Jimmy Olsson, KTH

Abstract

As one of the most powerful modeling tools of modern statistics, dynamical state-space models—also known as general state-space hidden Markov models—are today widely used in diverse areas such as pattern recognition, signal processing, bioinformatics, finance, and many more. In this talk, I will give a brief overview of statistical inference and learning in this kind of generative models, including statistical theory and computational algorithms. A special focus will be on sequential Monte Carlo methods, which today constitute a standard tool for sampling online from sequences of distributions, including those state posteriors that are of vital importance for inference and learning in state-space models. In this context, I will describe some of my own contributions to this field and present some open problems.