CONDITIONALLY GAUSSIAN RANDOM PROCESSES.

R. Sh Liptser*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

A class of non-Gaussian processes ( theta //t, xi //t, 0 less than equivalent to t less than equivalent to T) is defined by means of nonlinear Ito stochastic differential equations with the property that the conditional finite-dimensional distribution functions of the process ( theta //s, s less than equivalent to t) subject to the condition ( xi //s, s less than equivalent to t) are with probability 1 Gaussian. This fact yields effective results in statistical problems of random processes, in particular a nonlinear generalization of the Kalman - Bucy filtering problem.

Original languageEnglish
Pages (from-to)151-167
Number of pages17
JournalProblems of Information Transmission
Volume10
Issue number2
StatePublished - 1974

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