Skip to main content Accessibility help
×
Home
Hostname: page-component-544b6db54f-8tjh8 Total loading time: 0.259 Render date: 2021-10-23T23:58:22.860Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": true, "newCiteModal": false, "newCitedByModal": true, "newEcommerce": true, "newUsageEvents": true }

1 - Introduction

Published online by Cambridge University Press:  05 June 2012

Robert M. Gray
Affiliation:
Stanford University, California
Lee D. Davisson
Affiliation:
University of Maryland, College Park
Get access

Summary

A random or stochastic process is a mathematical model for a phenomenon that evolves in time in an unpredictable manner from the viewpoint of the observer. The phenomenon may be a sequence of real-valued measurements of voltage or temperature, a binary data stream from a computer, a modulated binary data stream from a modem, a sequence of coin tosses, the daily Dow–Jones average, radiometer data or photographs from deep space probes, a sequence of images from a cable television, or any of an infinite number of possible sequences, waveforms, or signals of any imaginable type. It may be unpredictable because of such effects as interference or noise in a communication link or storage medium, or it may be an information-bearing signal, deterministic from the viewpoint of an observer at the transmitter but random to an observer at the receiver.

The theory of random processes quantifies the above notions so that one can construct mathematical models of real phenomena that are both tractable and meaningful in the sense of yielding useful predictions of future behavior. Tractability is required in order for the engineer (or anyone else) to be able to perform analyses and syntheses of random processes, perhaps with the aid of computers. The “meaningful” requirement is that the models must provide a reasonably good approximation of the actual phenomena. An oversimplified model may provide results and conclusions that do not apply to the real phenomenon being modeled.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2004

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Send book to Kindle

To send this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • Introduction
  • Robert M. Gray, Stanford University, California, Lee D. Davisson, University of Maryland, College Park
  • Book: An Introduction to Statistical Signal Processing
  • Online publication: 05 June 2012
  • Chapter DOI: https://doi.org/10.1017/CBO9780511801372.003
Available formats
×

Send book to Dropbox

To send content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about sending content to Dropbox.

  • Introduction
  • Robert M. Gray, Stanford University, California, Lee D. Davisson, University of Maryland, College Park
  • Book: An Introduction to Statistical Signal Processing
  • Online publication: 05 June 2012
  • Chapter DOI: https://doi.org/10.1017/CBO9780511801372.003
Available formats
×

Send book to Google Drive

To send content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about sending content to Google Drive.

  • Introduction
  • Robert M. Gray, Stanford University, California, Lee D. Davisson, University of Maryland, College Park
  • Book: An Introduction to Statistical Signal Processing
  • Online publication: 05 June 2012
  • Chapter DOI: https://doi.org/10.1017/CBO9780511801372.003
Available formats
×