APPLICATIONS OF PROCESS DISTORTION MEASURES TO PREDICTOR MISMATCH PROBLEMS AND CODING.

Yasuo Matsuyama

APPLICATIONS OF PROCESS DISTORTION MEASURES TO PREDICTOR MISMATCH PROBLEMS AND CODING.

Yasuo Matsuyama

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

Abstract

Linear least-square predictor mismatch problems are discussed as applications of stochastic process distortion measures. Linear predictor coding is also discussed in conjunction with distortion measures. First, nondeterministic and purely nondeterministic processes are defined and purely nondeterministic processes are expressed by autoregressive models. Then, ″robustness″ is defined when there exists mismatch between one-step predictor and true information source.

Original language	English
Pages (from-to)	28-36
Number of pages	9
Journal	Electronics & communications in Japan
Volume	62
Issue number	12
Publication status	Published - 1981 Dec
Externally published	Yes

ASJC Scopus subject areas

Engineering(all)

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title = "APPLICATIONS OF PROCESS DISTORTION MEASURES TO PREDICTOR MISMATCH PROBLEMS AND CODING.",

abstract = "Linear least-square predictor mismatch problems are discussed as applications of stochastic process distortion measures. Linear predictor coding is also discussed in conjunction with distortion measures. First, nondeterministic and purely nondeterministic processes are defined and purely nondeterministic processes are expressed by autoregressive models. Then, ″robustness″ is defined when there exists mismatch between one-step predictor and true information source.",

author = "Yasuo Matsuyama",

year = "1981",

month = dec,

language = "English",

volume = "62",

pages = "28--36",

journal = "Electronics and Communications in Japan",

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AB - Linear least-square predictor mismatch problems are discussed as applications of stochastic process distortion measures. Linear predictor coding is also discussed in conjunction with distortion measures. First, nondeterministic and purely nondeterministic processes are defined and purely nondeterministic processes are expressed by autoregressive models. Then, ″robustness″ is defined when there exists mismatch between one-step predictor and true information source.

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