Moment Generating Functions

Understanding Moment Generating Functions

Moment generating functions (MGFs) play a pivotal role in the field of probability theory and statistics, providing a powerful way to characterize the distribution of a random variable. They are particularly useful because they can uniquely determine the probability distribution if the MGF exists in an open interval around zero. Moreover, MGFs simplify the process of finding moments (expected values of powers) of a random variable, which are fundamental in understanding the shape and characteristics of its distribution.

Definition of Moment Generating Functions

The moment generating function of a random variable X is defined as a function M(t), where t is a real number, given by the expected value of etX. Mathematically, it can be expressed as:

M(t) = E[etX]

For continuous random variables, the MGF is the integral of e


times the probability density function (pdf) of the variable, over the entire space where the variable is defined. For discrete random variables, it is the sum of e


times the probability mass function (pmf).

Properties of Moment Generating Functions

Moment generating functions have several key properties that make them useful:

  • Uniqueness: If it exists, the MGF uniquely determines the probability distribution of a random variable. Two random variables with the same MGF have the same distribution.
  • Moments: The nth moment of the random variable is the nth derivative of the MGF evaluated at t = 0, if it exists. That is, the kth moment about the origin is given by E[Xk] = M(k)(0).
  • Additivity: If X and Y are independent random variables, the MGF of their sum is the product of their individual MGFs, i.e., MX+Y(t) = MX(t) * MY(t).

Applications of Moment Generating Functions

Moment generating functions are used in various applications:

  • Sum of Independent Random Variables:

    MGFs can be used to find the distribution of the sum of independent random variables, which is particularly useful in the central limit theorem and in actuarial science.

  • Characterization of Distributions:

    They are used to prove the properties of well-known distributions, such as the normal distribution, exponential distribution, and Poisson distribution.

  • Deriving Moments:

    MGFs provide a convenient way to derive moments, which are used to describe features of the distribution such as the mean, variance, skewness, and kurtosis.

Limitations of Moment Generating Functions

While MGFs are a powerful tool, they do have limitations:

  • MGFs do not always exist for all t. For some distributions, the MGF only exists for a range of t values around zero.
  • For heavy-tailed distributions, such as the Cauchy distribution, the MGF may not exist at all.

Examples of Moment Generating Functions

Here are a couple of examples of MGFs for common distributions:

  • Normal Distribution: For a normal random variable X with mean μ and variance σ2, the MGF is M(t) = exp(μt + (σ2t2/2)).
  • Exponential Distribution: For an exponential random variable X with rate parameter λ, the MGF is M(t) = λ / (λ - t) for t < λ.


Moment generating functions are a fundamental concept in probability and statistics with wide-ranging applications. They provide a unified approach to understanding the moments and distributions of random variables. Despite their limitations, MGFs remain an essential tool in the statistician's toolbox for theoretical and practical problem-solving.

Understanding MGFs can be challenging, but their ability to simplify complex problems and provide insights into the behavior of random phenomena makes them invaluable in statistical analysis and probability theory.

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