Hidden Markov Models: Identification, Inverse Filtering and Applications

Time: Tue 2020-06-02 09.00

Location: zoomlänk för disputation: https://kth-se.zoom.us/webinar/register/WN_94cOBU56TluzJktLxD9ZAQ (English)

Subject area: Electrical Engineering

Doctoral student: Robert Mattila , Reglerteknik

Opponent: Professor John Lygeros, ; Professor Jan H. van Schuppen, ; Professor Tobias Rydén, ; Assistant Professor Ayca Özcelikkale,

Supervisor: Professor Bo Wahlberg, Signaler, sensorer och system, Optimeringslära och systemteori, Reglerteknik; Associate Professor Cristian R. Rojas, Reglerteknik

Abstract

A hidden Markov model (HMM) comprises a state with Markovian dynamics that is hidden in the sense that it can only be observed via a noisy sensor. This thesis considers three themes in relation to HMMs, namely, identification, inverse filtering and applications.

In order to employ an HMM, its parameters have first to be identified (or, estimated) from data. Traditional maximum-likelihood estimation procedures may, in practice, suffer from convergence to bad local optima and high computational cost. Recently proposed methods of moments address these shortcomings, but are less accurate. We explore how such methods can be extended to incorporate non-consecutive correlations in data so as to improve their accuracy (while still retaining their attractive properties).

Motivated by applications in the design of counter-adversarial autonomous (CAA) systems, we then ask the question: Is it possible to estimate the parameters of an HMM from other data sources than just raw measurements from its sensor? To answer this question, we consider a number of inverse filtering problems. First, we demonstrate how HMM parameters and sensor measurements can be reconstructed from posterior distributions from an HMM filter. Next, we show how to estimate such posterior distributions from actions taken by a rational agent. Finally, we bridge our results to provide a solution to the CAA problem of remotely estimating the accuracy of an adversary’s sensor based on its actions.

Throughout the thesis, we motivate our results with applications in various domains. A real-world application that we investigate in particular detail is how the treatment of abdominal aortic aneurysms can be modeled in the Markovian framework. Our findings suggest that the structural properties of the optimal treatment policy are different than those recommended by current clinical guidelines – in particular, that younger patients could benefit from earlier surgery. This indicates an opportunity for improved care of patients with the disease.

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