Expected Value

Description

The Expected Value of a probability density function is

(1)
\begin{align} E(X) = \int_{-\infty}^{\infty} x f(x) dx. \end{align}

The Sage cell below calculates the expected value of the exponential distribution when $\lambda=4$.

Sage Cell

Code

l=4
f(X)=l*exp(-l*x)
E(X)=integral(x*f(X),x,0,+infinity)
E(X)

Options

Expected Value for Discrete Distributions

The Expected Value of a discrete probability distribution function is

(2)
\begin{align} E(X) = \sum_{x=0}^{\infty} x f(x) dx. \end{align}

The Sage cell below calculates the expected value of the poisson distribution when $\lambda=15$.

Code

lamb = 15
var('k')
f(k)=exp( -lamb) * (( lamb^k ) / ( factorial(k)))
sum(k * f(k) , k, 0, infinity)

Tags

Primary Tags: Probability

Secondary Tags: Random Variables: Expectation, Continuous Distributions, Discrete Distributions

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Date: 16 Oct 2018 17:15

Submitted by: James A Phillips

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