KTH Matematik  

Matematisk Statistik

Tid: 5 juni 2015 kl 14.00.

Seminarierummet 3721, Institutionen för matematik, KTH, Lindstedtsvägen 25, plan 7. Karta!

Föredragshållare: Johan Westerborn

Titel: On particle-based online smoothing and parameter inference in general hidden Markov models (Licentiatseminarium) Diskutant: Thomas Schön, Uppsala Univesitet

Abstract This thesis consists of two papers studying online inference in general hidden Markov models using sequential Monte Carlo methods. The first paper present an novel algorithm, the particle-based, rapid incremental smoother (PaRIS), aimed at efficiently perform online approximation of smoothed expectations of additive state functionals in general hidden Markov models. The algorithm has, under weak assumptions, linear computational complexity and very limited memory requirements. The algorithm is also furnished with a number of convergence results, including a central limit theorem. The second paper focuses on the problem of online estimation of parameters in a general hidden Markov model. The algorithm is based on a forward implementation of the classical expectation-maximization algorithm. The algorithm uses the PaRIS algorithm to achieve an efficient algorithm.

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Sidansvarig: Filip Lindskog
Uppdaterad: 25/02-2009