Discrete Probability Distribution

Discrete probability distribution counts occurrences with finite outcomes. The common examples of discrete probability distribution include Bernoulli, Binomial and Poisson distributions.

In this article we will explore discrete probability distribution along with discrete probability distribution definition, discrete probability distribution condition and discrete probability distribution formulas.

A probability distribution that gives the finite trials of a discrete random variable at a given point in time is called a discrete probability distribution. The probability distribution gives the different values of a random variable along with its different probabilities. The two types of probability distribution include discrete probability distribution and continuous probability distribution.

Discrete Probability Distribution Definition

Discrete probability distribution is defined as the probability at a specific value for a discrete random variable. The discrete probability distributions represent the probability distributions with finite outcomes.

Conditions for Discrete Probability Distribution

Conditions for the discrete probability distribution are:

• Probability of a discrete random variable lies between 0 and 1: 0 ≤ P (X = x) ≤ 1

• Sum of Probabilities is always equal to 1: ∑ P (X =x) = 1

Discrete Probability Distribution Example

Let two coins be tossed then the probability of getting a tail is an example of a discrete probability distribution. The sample space for the given event is {HH, HT, TH, TT} and X be the number of tails then, the discrete probability distribution table is given by:

The different formulas for the discrete probability distribution like probability mass function, cumulative distribution function, mean and variance are given below.

PMF of Discrete Probability Distribution

PMF of a discrete random variable X is the value completely equal to x. The PMF i.e., probability mass function of discrete probability distribution is given by:

f(x) = P (X = x)

CDF of Discrete Probability Distribution

CDF of a discrete random variable X is less than or equal to value x. The CDF i.e., cumulative distribution function of discrete probability distribution is given by:

f(x) = P (X ≤ x)

Mean of discrete probability distribution is the average of all the values that a discrete variable can obtain. It is also called as the expected value of the discrete probability distribution. The mean of discrete probability distribution is given by:

E[X] = ∑x P(X =x)

Variance of discrete probability distribution is defined as the product of squared difference of distribution and mean with PMF. The variance of the discrete probability distribution is given by:

Var[X] = ∑(x – μ)² P(X = x)

Steps to find the discrete probability function are given below:

• Step 1: First determine the sample space of the given event.

• Step 2: Define random variable X as the event for which the probability has to be found.

• Step 3: Consider the possible values of x and find the probabilities for each value.

• Step 4: Write all the values of x and their respective probabilities in tabular form to get the discrete probability distribution.

The different types of discrete probability distribution are listed below.

• Bernoulli Distribution
• Binomial Distribution
• Poisson Distribution
• Geometric Distribution

Bernoulli Distribution

A discrete probability distribution with the probability of success p if the value of random variable is 1 and the probability of failure 1-p if the value of random variable is zero is called the Bernoulli distribution. The probability mass function of the Bernoulli distribution is given by:

P (X = x) = [Tex]\bold{\begin{cases} \bold{p, \hspace{0.1cm} x = 1}\\ \bold{1-p, \hspace{0.1cm} x = 0} \end{cases}}[/Tex]

Binomial Distribution

A discrete probability distribution that includes the number of trials n, probability of success and probability of failure is called as Binomial distribution. The probability mass function of the Binomial distribution is given by:

P (X = x) = ⁿC_x p^x (1-p) ^n-x

Poisson Distribution

A discrete probability distribution that gives the number of events occurred at a specific time period with the help of its mean is called as the Poisson distribution. The probability mass function of Poisson distribution is given by:

P (X = x) = [ƛ^x × e^-ƛ] / x!

Geometric Distribution

A discrete probability distribution that includes the successive failure probability until the success probability is encountered is called as Geometric distribution. The probability mass function of the geometric distribution is given by:

P (X = x) = (1 – p) ^xp

Example 1: Construct the discrete probability table when a coin is tossed two times and X be random variable representing the number of one head.

Solution:

Sample space of two coin tossed = 4 i.e., {HH, HT, TH, TT}

X: Number of one head

The below table represents the discrete probability.

Example 2: Find the value of p from the given discrete probability table.

Solution:

To find the value of p we will use the discrete probability condition.

∑ P (X =x) = 1

0.1 + p + 0.2 + 0.4 = 1

0.7 + p = 1

p = 1 – 0.7

p = 0.3

Example 3: Find the mean of discrete probability distribution using below table.

Solution:

To find the mean of discrete probability distribution we use formula:

E[X] = ∑ [ x P(X =x)]

E[X] = 2 × 0.16 + 3 × 0.45 + 4 × 0.32 + 5 × 0.07

E[X] = 0.32 + 1.35 + 1.28 + 0.35

E[X] = 3.3

Example 4: If there are 15 pens in which 3 pens are defective and the probability of pen is defective 0.5 then, find the discrete probability of pen to be defective.

Solution:

To find the required probability we use Binomial Distribution

P (X = x) = ⁿC_x p^x (1-p) ^n-x

P (X = 3) = ¹⁵C₃ p³ (1-p) ^15-3

P (X = 3) = ¹⁵C₃ (0.5)³ (1 – 0.5) ¹²

P (X = 3) = 455 × (0.5)³ × (0.5) ¹²

P (X = 3) = 455 × (0.5)¹⁵

P (X = 3) = 0.014

Q1. Construct the discrete probability table when a dice is rolled, and X be random variable representing the numbers greater than equal to 3.

Q2. Find the value of a from the given discrete probability table.

Q3. Find the expected value of discrete probability distribution using below table.

Q4. Determine the probability if the number of trials is 100, number of successes is 94 and the probability of failure is 0.4.

What is Discrete Probability Distribution?

A probability distribution at specific time for the values and probabilities of discrete random variables is called discrete probability distribution.

What are Requirements of Discrete Probability Distribution?

Requirements of discrete probability distribution are as follows:

• Probability of discrete random variable lies between 0 and 1: 0 ≤ P (X = x) ≤ 1
• Sum of probabilities is always equal to 1: ∑P (X = x) = 1

What is Discrete and Continuous Probability Distribution?

Discrete probability distribution deals with the values and probabilities of discrete random variables at specific time whereas the continuous probability distribution deals with the values and probabilities of continuous random variables at specified interval.

Can Expected Value of Discrete Probability Distribution be Negative?

Yes, expected value of discrete probability distribution can be negative.