Probability Mass Function
What is Probability Mass Function?
Probability of the discrete random variables that equals to some value is called as the probability mass function.
What is PMF and PDF?
PMF gives the probability of discrete random variables whereas the PDF is the probability of continuous random variables.
What are Properties of Probability Mass Function?
Properties of probability mass function are:
- f(x) = P (X = x) > 0 i.e., probability is always positive.
- ∑x∈ S f(x) = 1 i.e., sum of all probabilities equals to 1.
- P (X ∈ E) = ∑x∈ E f(x) i.e., probability of event E is given by sum of probabilities of values of x in E.
Can Probability Mass Function be Greater Than 1?
Probability mass function cannot be greater than 1 because probability lies between 0 and 1.
Can PMF be Negative?
No, PMF cannot be negative as probability cannot be negative.
Probability Mass Function
Probability mass function i.e., PMF is the probability of discrete random variables with fixed values. In this article we will see the probability mass function along with the PMF definition, probability mass function examples, properties of probability mass function and probability mass function formulas.
We will also discuss the probability mass function table and graph, the difference between the probability mass function and probability density function and solve some examples related to the probability mass function. Let’s start our learning on the topic “Probability Mass Function”.
Table of Content
- What is Probability Mass Function?
- Probability Mass Function Definition
- Probability Mass Function Example
- Probability Mass Function Formulas
- Probability Mass Function Formula in Binomial Distribution
- Probability Mass Function Formula in Poisson Distribution
- Probability Mass Function Table and Graph
- Properties of Probability Mass Function
- Difference Between Probability Mass Function and Probability Density Function
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