Advisor

Andrew M. Fraser

Date of Award

7-5-1995

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Electrical Engineering

Department

Electrical Engineering

Physical Description

1 online resource (v, 100 p.)

Subjects

Markov processes -- Computer programs, Precipitation variability -- Computer programs

DOI

10.15760/etd.6932

Abstract

Novel seasonal hidden Markov models (SHMMs) for stochastic time series with periodically varying characteristics are developed. Nonlinear interactions among SHMM parameters prevent the use of the forward-backward algorithms which are usually used to fit hidden Markov models to a data sequence. Instead, Powell's direction set method for optimizing a function is repeatedly applied to adjust SHMM parameters to fit a data sequence. SHMMs are applied to a set of meteorological data consisting of 9 years of daily rain gauge readings from four sites. The fitted models capture both the annual patterns and the short term persistence of rainfall patterns across the four sites.

Description

If you are the rightful copyright holder of this dissertation or thesis and wish to have it removed from the Open Access Collection, please submit a request to pdxscholar@pdx.edu and include clear identification of the work, preferably with URL

Persistent Identifier

https://archives.pdx.edu/ds/psu/29137

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