Using Continuous Uniform MGF to find E(X)

In summary: That is, we want to find the coefficients c_n in\frac{e^{bz} - e^{az}}{z} = c_0 + c_1 z + c_2 z^2 + \cdots, where e^{az} = 1 + az + \frac{(az)^2}{2} + \cdots, e^{bz} = 1 + bz + \frac{(bz)^2}{2} + \cdots. The result is that \frac{e^{bz} - e^{az}}{z} = (b - a) + \frac{(b^2 - a^2)}{2!} z + \frac{(b^
  • #1
Darth Frodo
212
1


Continuous Uniform MGF is [itex]M_{x}(z) = E(e^zx) = \frac{e^{zb} - e^{za}}{zb - za}[/itex]

[itex]\frac{d}{dz}M_{x}(z) = E(X)[/itex]

Using the Product Rule

[itex]\ U = e^{bz} - e^{az}[/itex]

[itex]\ V = (zb - za)^{-1}[/itex]

[itex]\ U' = be^{bz} - ae^{az}[/itex]

[itex]\ V' = -1(zb - za)^{-2}(b - a)[/itex]

[itex]\frac{dM}{dz} = UV' + VU'[/itex]

[itex]\frac{dM}{dz} = (e^{bz} - e^{az})(-1(zb - za)^{-2}(b - a)) + ((zb - za)^{-1})(be^{bz} - ae^{az})[/itex] evaluated at z = 1


[itex]\ (e^{b}-e^{a})(-1)(b - a)^{-2}(b - a) + (b - a)^{-1}(be^{b} - ae^{a})[/itex]


[itex]\frac{e^{a} - e^{b} + be^{b} - ae^{a} }{b - a}[/itex]


The answer is [itex]\frac{b + a}{2}[/itex]


I'd really appreciate it if someone could tell me where I'm going wrong. Thanks.
 
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  • #2
You didn't define your terms. What is MGF and what is E(X). These may be standard, but I don't know them.
 
  • #3
Moment Generating Function and Expected Value of the Continuous random variable X
 
  • #4
Darth Frodo said:
Continuous Uniform MGF is [itex]M_{x}(z) = E(e^zx) = \frac{e^{zb} - e^{za}}{zb - za}[/itex]

[itex]\frac{d}{dz}M_{x}(z) = E(X)[/itex]

Using the Product Rule

[itex]\ U = e^{bz} - e^{az}[/itex]

[itex]\ V = (zb - za)^{-1}[/itex]

[itex]\ U' = be^{bz} - ae^{az}[/itex]

[itex]\ V' = -1(zb - za)^{-2}(b - a)[/itex]

[itex]\frac{dM}{dz} = UV' + VU'[/itex]

[itex]\frac{dM}{dz} = (e^{bz} - e^{az})(-1(zb - za)^{-2}(b - a)) + ((zb - za)^{-1})(be^{bz} - ae^{az})[/itex] evaluated at z = 1


[itex]\ (e^{b}-e^{a})(-1)(b - a)^{-2}(b - a) + (b - a)^{-1}(be^{b} - ae^{a})[/itex]


[itex]\frac{e^{a} - e^{b} + be^{b} - ae^{a} }{b - a}[/itex]


The answer is [itex]\frac{b + a}{2}[/itex]


I'd really appreciate it if someone could tell me where I'm going wrong. Thanks.

You need to evaluate M'(z) at z = 0, not at z = 1! Of course, you need to worry about the fact that your formula for M(z) does not apply right at z = 0, so you need to look at limits as z → 0, and use l'Hospital's rule, for example. BTW: the formula you wrote for EX is wrong; it should be
[tex] EX = \left. \frac{d M(z)}{dz} \right|_{z = 0} = M'(0).[/tex] For ##z \neq 0## the derivative of M does not have any particular relation to EX.

However, that would be doing it the hard way. From
[tex] M_X(z) = E e^{zX} = 1 + z \,EX + \frac{z^2}{2} E X^2 + \cdots, [/tex]
we see that we can get the moments of X from the moment-generating-function---that's why it is so named---so all you need is the series expansion of M(z) around z = 0 (that is, the Maclaurin series). That is most easily obtained by just getting the series expansion of the numerator, then dividing by z.
 
Last edited:

Related to Using Continuous Uniform MGF to find E(X)

What is a continuous uniform distribution?

A continuous uniform distribution is a probability distribution which has a constant probability density function over a certain interval. This means that all values within the interval have an equal chance of occurring.

What is the moment generating function (MGF) for a continuous uniform distribution?

The MGF for a continuous uniform distribution is given by M(t) = (etb - eta)/(t(b-a)), where a and b are the lower and upper bounds of the interval.

How do you use the continuous uniform MGF to find the expected value (E(X))?

To find the expected value using the continuous uniform MGF, you can simply take the first derivative of the MGF and evaluate it at t=0. This will give you the mean value of your continuous uniform distribution.

What is the significance of using the MGF to find E(X) for a continuous uniform distribution?

The MGF allows us to find the expected value for any continuous distribution, not just the continuous uniform distribution. It also provides a convenient way to summarize the properties of a distribution through its moments.

Are there any limitations to using the continuous uniform MGF to find E(X)?

The continuous uniform MGF can only be used for continuous uniform distributions, and may not be applicable for other types of distributions. Additionally, it may be difficult to calculate the MGF for more complex distributions with multiple parameters.

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