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  • CSE & eScience Content

    This module is included inLens: Computational Science and Engineering and eScience Content
    By: Jan E. OdegardAs a part of collection:"Random Processes"

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Expectation

Module by: Nick Kingsbury. E-mail the author

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Summary: This module introduces expectation.

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Expectations form a fundamental part of random signal theory. In simple terms the Expectation Operator calculates the mean of a random quantity although the concept turns out to be much more general and useful than just this.

If XX has pdf f X x f X x (correctly normalised so that - f X xdx=1 x f X x 1 ), its expectation is given by:

EX=-x f X xdx=X¯ X x x f X x X (1)
For discrete processes, we substitute this previous equation in here to get
EX=-xi=1M p X x i δx x i dx=i=1M x i p X x i =X¯ X x x i 1 M p X x i δ x x i i 1 M x i p X x i X (2)
Now, what is the mean value of some function, Y=gX Y g X ?

Using the result of this previous equation for pdfs of related processes YY and X X:

f Y yy= f X xx f Y y y f X x x (3)
Hence (again assuming infinite integral limits unless stated otherwise)
EgX=EY=y f Y ydy=gx f X xdx g X Y y y f Y y x g x f X x (4)
This is an important result which allows us to use the Expectation Operator for many purposes including the calculation of moments and other related parameters of a random process.

Note, expectation is a Linear Operator:

Ea g 1 X+b g 2 X=aE g 1 X+bE g 2 X a g 1 X b g 2 X a g 1 X b g 2 X (5)

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A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

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What are tags? tag icon

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

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