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A Comparison of Serial & Parallel Particle Filters for Time Series Analysis

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Date
2014
Author
Klemish, David
Advisor
Rubio-Ramírez, Juan F
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Abstract

This paper discusses the application of parallel programming techniques to the estimation of hidden Markov models via the use of a particle filter. It highlights how the Thrust parallel programming

language can be used to implement a particle filter in parallel. The impact of a parallel particle filter on the running times of three different models is investigated. For particle filters using a large number

of particles, Thrust provides a speed-up of five to ten times over a serial C++ implementation, which is less than reported in other research.

Type
Master's thesis
Department
Statistical and Economic Modeling
Subject
Statistics
Permalink
https://hdl.handle.net/10161/8845
Citation
Klemish, David (2014). A Comparison of Serial & Parallel Particle Filters for Time Series Analysis. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/8845.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.

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