Relations between the likehood ratios for two-dimensional continuous and discrete stochastic processes

R. Luesink; Rob Luesink

doi:10.1109/CDC.1991.261617

Relations between the likehood ratios for two-dimensional continuous and discrete stochastic processes

R. Luesink, Rob Luesink

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

The author considers the likelihood ratio for 2D processes. In order to detect this ratio, it is necessary to compute the determinant of the covariance operator of the signal-plus-noise observation process. In the continuous case, this is in general a difficult problem. For cyclic processes, using Fourier transforms it is possible to compute the determinant for continuous and discrete processes. For the 2D Poisson equation and its discretization, it is shown that the discretized determinant converges to the continuous one if the stepsize tends to zero

Original language	Undefined
Title of host publication	30th IEEE Conference on Decisions and Control
Place of Publication	Brighton, U.K.
Publisher	IEEE
Pages	2394-2395
Number of pages	2
ISBN (Print)	9780780304505
DOIs	https://doi.org/10.1109/CDC.1991.261617
Publication status	Published - 11 Dec 1991
Event	30th IEEE Conference on Decision and Control, CDC 1991 - Brighton, United Kingdom Duration: 11 Dec 1991 → 13 Dec 1991 Conference number: 30

Publication series

Name
Publisher	IEEE
Volume	3

Conference

Conference	30th IEEE Conference on Decision and Control, CDC 1991
Abbreviated title	CDC
Country/Territory	United Kingdom
City	Brighton
Period	11/12/91 → 13/12/91

Keywords

METIS-141564
IR-30923

Access to Document

10.1109/CDC.1991.261617

00261617

Cite this

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title = "Relations between the likehood ratios for two-dimensional continuous and discrete stochastic processes",

abstract = "The author considers the likelihood ratio for 2D processes. In order to detect this ratio, it is necessary to compute the determinant of the covariance operator of the signal-plus-noise observation process. In the continuous case, this is in general a difficult problem. For cyclic processes, using Fourier transforms it is possible to compute the determinant for continuous and discrete processes. For the 2D Poisson equation and its discretization, it is shown that the discretized determinant converges to the continuous one if the stepsize tends to zero",

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AU - Luesink, Rob

N1 - Conference code: 30

PY - 1991/12/11

Y1 - 1991/12/11

N2 - The author considers the likelihood ratio for 2D processes. In order to detect this ratio, it is necessary to compute the determinant of the covariance operator of the signal-plus-noise observation process. In the continuous case, this is in general a difficult problem. For cyclic processes, using Fourier transforms it is possible to compute the determinant for continuous and discrete processes. For the 2D Poisson equation and its discretization, it is shown that the discretized determinant converges to the continuous one if the stepsize tends to zero

AB - The author considers the likelihood ratio for 2D processes. In order to detect this ratio, it is necessary to compute the determinant of the covariance operator of the signal-plus-noise observation process. In the continuous case, this is in general a difficult problem. For cyclic processes, using Fourier transforms it is possible to compute the determinant for continuous and discrete processes. For the 2D Poisson equation and its discretization, it is shown that the discretized determinant converges to the continuous one if the stepsize tends to zero

KW - METIS-141564

KW - IR-30923

U2 - 10.1109/CDC.1991.261617

DO - 10.1109/CDC.1991.261617

M3 - Conference contribution

SN - 9780780304505

SP - 2394

EP - 2395

BT - 30th IEEE Conference on Decisions and Control

PB - IEEE

CY - Brighton, U.K.

Y2 - 11 December 1991 through 13 December 1991

ER -