Detection and prediction of a stochastic process having multiple hypotheses

D. W. Kelsey; A. H. Haddad

doi:10.1016/0020-0255(73)90045-5

Detection and prediction of a stochastic process having multiple hypotheses

D. W. Kelsey, A. H. Haddad^*

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

A system is proposed which combines hypothesis testing and prediction to estimate the future value of a stochastic process whose statistics are known only to belong to some finite set of possible hypotheses. Bayes optimization of the individual components is performed, and system performance is discussed for a modified version of the usual mean-squared error predictor cost functions. An example is given illustrating various features of the system's performance for a specific choice of input hypotheses.

Original language	English (US)
Pages (from-to)	301-311
Number of pages	11
Journal	Information Sciences
Volume	6
Issue number	C
DOIs	https://doi.org/10.1016/0020-0255(73)90045-5
State	Published - 1973

ASJC Scopus subject areas

Software
Control and Systems Engineering
Theoretical Computer Science
Computer Science Applications
Information Systems and Management
Artificial Intelligence

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10.1016/0020-0255(73)90045-5

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title = "Detection and prediction of a stochastic process having multiple hypotheses",

abstract = "A system is proposed which combines hypothesis testing and prediction to estimate the future value of a stochastic process whose statistics are known only to belong to some finite set of possible hypotheses. Bayes optimization of the individual components is performed, and system performance is discussed for a modified version of the usual mean-squared error predictor cost functions. An example is given illustrating various features of the system's performance for a specific choice of input hypotheses.",

author = "Kelsey, {D. W.} and Haddad, {A. H.}",

note = "Funding Information: *This work was supported by the Joint Electronics Program under contract DAAB-O{\textquoteright}I-67-C-0199. t Please send all correspondence to A. H. Haddad at the above address-after August 1, 1973; prior to Aug. 1, {\textquoteleft}73, send correspondence to: A. H. Haddad, c/o School of Engineering, Tel-Aviv University, Ramat-Aviv, Tel-Aviv, Israel.",

year = "1973",

doi = "10.1016/0020-0255(73)90045-5",

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T1 - Detection and prediction of a stochastic process having multiple hypotheses

AU - Kelsey, D. W.

AU - Haddad, A. H.

N1 - Funding Information: *This work was supported by the Joint Electronics Program under contract DAAB-O’I-67-C-0199. t Please send all correspondence to A. H. Haddad at the above address-after August 1, 1973; prior to Aug. 1, ‘73, send correspondence to: A. H. Haddad, c/o School of Engineering, Tel-Aviv University, Ramat-Aviv, Tel-Aviv, Israel.

PY - 1973

Y1 - 1973

N2 - A system is proposed which combines hypothesis testing and prediction to estimate the future value of a stochastic process whose statistics are known only to belong to some finite set of possible hypotheses. Bayes optimization of the individual components is performed, and system performance is discussed for a modified version of the usual mean-squared error predictor cost functions. An example is given illustrating various features of the system's performance for a specific choice of input hypotheses.

AB - A system is proposed which combines hypothesis testing and prediction to estimate the future value of a stochastic process whose statistics are known only to belong to some finite set of possible hypotheses. Bayes optimization of the individual components is performed, and system performance is discussed for a modified version of the usual mean-squared error predictor cost functions. An example is given illustrating various features of the system's performance for a specific choice of input hypotheses.

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VL - 6

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JO - Information Sciences

JF - Information Sciences

IS - C

ER -

Detection and prediction of a stochastic process having multiple hypotheses

Abstract

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