#### Author:

John B. Thomas,Saleem A. Kassam

#### Language:

#### Category:

#### Subcategory:

#### ePub size:

1592 kb

#### Other formats:

lit mobi mbr txt

#### Rating:

4.1

#### Publisher:

Springer; 1 edition (December 17, 1987)

#### Pages:

234

#### ISBN:

0387966803

# Signal Detection in Non-Gaussian Noise (Springer Texts in Electrical Engineering) e-book

#### by John B. Thomas,Saleem A. Kassam

Автор: Saleem A. Kassam; John B. Thomas Название: Signal Detection . Описание: Detection of Random Signals in Dependent Gaussian Noise.

This book deals with the non-Gaussian distributions and addresses the consequences of non-normality and time dependency in asset returns and option prices.

Start by marking Signal Detection in Non-Gaussian Noise as Want to. .

Start by marking Signal Detection in Non-Gaussian Noise as Want to Read: Want to Read savin. ant to Read. Read by Saleem A. Kassam. This book contains a unified treatment of a class of problems of signal detection theory. This is the detection of signals in additive noise which is not required to have Gaussian probability density functions in its statistical description. For the most part the material developed here can be classified as belonging to the general body of results of parametric theory. Thu This book contains a unified treatment of a class of problems of signal detection theory.

Springer Texts in Electrical Engineering. Three canonical problems of signal detection in additive noise are covered here. This is the detection of signals in addi tive noise which is not required to have Gaussian probability den sity functions in its statistical description.

Saleem A. Kassam, John B. Thomas. This book contains a unified treatment of a class of problems of signal detection theory

Saleem A. This is the detection of signals in addi- tive noise which is not required to have Gaussian probability den- sity functions in its statistical description. For the most part the material developed here can be classified as belonging to the gen- eral body of results of parametric theory. Thus the probability density functions of the observations are assumed to be known, at least to within a finite number of unknown parameters in a known functional form.

Non - Gaussian Noise . Poor Introduction to Shannon Sampling and Interpolation Theory . Snyder /M. I. Miller Linear System Theory . Desoer Advanced Topics in Shannon Sampling.