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Numerical Instability of Particle Learning: a case study
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Numerisk instabilitet i Particle Learning: en fallstudie (Swedish)
Abstract [en]

This master's thesis is about a method called Particle Learning (PL) which can be used to analyze so called hidden Markov models (HMM) or, with an alternative terminology, state-space models (SSM) which are very popular for modeling time series. The advantage of PL over more established methods is its capacity to process new datapoints with a constant demand on computational resources but it has been suspected to su er from a problem known as particle path degeneracy. The purpose with this report is to investigate the degeneracy of PL by testing it on two examples. The results suggest that the method may not work very well for long time series.

Abstract [sv]

Detta examensarbete handlar om en metod som kallas Particle Learning (PL) som kan användas för att analysera dolda Markovmodeller eller hidden Markov models (HMM), vilka med en alternativ terminologi även kallas tillståndsmodeller, som är mycket populära för att modellera tidsserier. Fördelen med PL över mera etablerade metoder är dess förmåga att bearbeta nya datapunkter med konstant behov av beräkningskapacitet men den har även misstänkts lida av ett problem känt som är känt som degenerering av partikelbanorna. Syftet med denna rapport är att undersöka degenereringen av PL genom att testa den på två exempel. Resultaten tyder på att metoden inte fungerar så bra för långa tidsserier.

Place, publisher, year, edition, pages
2016.
Series
TRITA-MAT-E, 2016:47
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-190983OAI: oai:DiVA.org:kth-190983DiVA: diva2:954405
Subject / course
Mathematical Statistics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2016-08-22 Created: 2016-08-19 Last updated: 2016-08-22Bibliographically approved

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