Mathematics | Probability Distributions Set 1 (Uniform Distribution)
Last Updated :
01 Mar, 2024
Prerequisite – Random Variable
In probability theory and statistics, a
probability distribution is a mathematical function that can be thought of as providing the probabilities of occurrence of different possible outcomes in an experiment. For instance, if the random variable [Tex]X[/Tex] is used to denote the outcome of a coin toss (“the experiment”), then the probability distribution of [Tex]X[/Tex] would take the value 0.5 for [Tex]X[/Tex] = heads, and 0.5 for [Tex]X[/Tex] = tails (assuming the coin is fair).
Probability distributions are divided into two classes –
- Discrete Probability Distribution – If the probabilities are defined on a discrete random variable, one which can only take a discrete set of values, then the distribution is said to be a discrete probability distribution. For example, the event of rolling a die can be represented by a discrete random variable with the probability distribution being such that each event has a probability of [Tex]\:\frac{1}{6}[/Tex].
- Continuous Probability Distribution – If the probabilities are defined on a continuous random variable, one which can take any value between two numbers, then the distribution is said to be a continuous probability distribution. For example, the temperature throughout a given day can be represented by a continuous random variable and the corresponding probability distribution is said to be continuous.
Cumulative Distribution Function –
Similar to the probability density function, the
cumulative distribution function [Tex]F(x)[/Tex] of a real-valued random variable X, or just distribution function of [Tex]X[/Tex] evaluated at [Tex]x[/Tex], is the probability that [Tex]X[/Tex] will take a value less than or equal to [Tex]x[/Tex].
For a discrete Random Variable,
[Tex]
F(x) = P(X\leq x) = \sum \limits_{x_0\leq x} P(x_0)
[/Tex]
For a continuous Random Variable,
[Tex]
F(x) = P(X\leq x) = \int \limits_{-\infty}^{x} f(x)dx
[/Tex]
Uniform Probability Distribution –
The Uniform Distribution, also known as the
Rectangular Distribution, is a type of Continuous Probability Distribution.
It has a Continuous Random Variable [Tex]X[/Tex] restricted to a finite interval [Tex][a,b][/Tex] and it’s probability function [Tex]f(x)[/Tex] has a constant density over this interval.
The Uniform probability distribution function is defined as-
[Tex]
f(x) =
\begin{cases}
\frac{1}{b-a}, & a\leq x \leq b\\
0, & \text{otherwise}\\
\end{cases}
[/Tex]
Expected or Mean Value – Using the basic definition of Expectation we get –
[Tex]
\begin{align*}
E(x) &= \int \limits_{-\infty}^{\infty} xf(x) dx&\\
&= \int \limits_{a}^{b} \frac{x}{b-a} dx&\\
&= \frac{1}{b-a} \int \limits_{a}^{b} x dx&\\
&= \frac{1}{b-a} \Big[ \frac{x^2}{2}\Big]_{a}^{b}&\\
&= \frac{b^2 – a^2}{2(b-a)}&\\
&= \frac{b + a}{2}&\\
\end{align*}
[/Tex]
Variance- Using the formula for variance- [Tex]V(X) = E(X^2) – (E(X))^2[/Tex]
[Tex]
\begin{align*}
E(x^2) &= \int \limits_{-\infty}^{\infty} x^2f(x) dx&\\
&= \int \limits_{a}^{b} \frac{x^2}{b-a} dx&\\
&= \frac{1}{b-a} \int \limits_{a}^{b} x^2 dx&\\
&= \frac{1}{b-a} \Big[ \frac{x^3}{3}\Big]_{a}^{b}&\\
&= \frac{b^3 – a^3}{3(b-a)}&\\
&= \frac{b^2 + a^2 + ab}{3}&\\
\end{align*}
[/Tex]
Using this result we get –
[Tex]
\begin{align*}
V(x) &= \frac{b^2 + a^2 + ab}{3} – \Big( \frac{b+a}{2}\Big) ^2 &\\
&= \frac{b^2 + a^2 + ab}{3} – \frac{b^2+a^2+2ab}{4} &\\
&= \frac{4b^2 + 4a^2 + 4ab – 3b^2 – 3a^2 – 6ab}{12}&\\
&= \frac{(b-a)^2}{12}&\\
\end{align*}
[/Tex]
Standard Deviation – By the basic definition of standard deviation,
[Tex]
\begin{align*}
\sigma &= \sqrt{V(x)} \\&= \frac{b-a}{2\sqrt{3}}
\end{align*}
[/Tex]
- Example 1 – The current (in mA) measured in a piece of copper wire is known to follow a uniform distribution over the interval [0, 25]. Find the formula for the probability density function [Tex]f(x)[/Tex] of the random variable [Tex]X[/Tex] representing the current. Calculate the mean, variance, and standard deviation of the distribution and find the cumulative distribution function [Tex]F(x)[/Tex].
- Solution – The first step is to find the probability density function. For a Uniform distribution, [Tex]f(x) = \frac{1}{b-a}[/Tex], where [Tex]b,\:a[/Tex] are the upper and lower limit respectively.
[Tex]
\therefore
\[
f(x) =
\begin{cases}
\frac{1}{25-0} = 0.04, & 0\leq x\leq 25 \\
0, & \text{otherwise} \\
\end{cases}
\]
[/Tex]
The expected value, variance, and standard deviation are-
[Tex]
E(x) = \frac{b+a}{2} = \frac{25+0}{2} = 12.5 mA\\\\
V(X) = \frac{(b-a)^2}{12} = \frac{(25-0)^2}{12} = 52.08 mA^2\\\\
\text{Standard Deviation} = \sigma = \sqrt{V(x)} = \frac{25}{2\sqrt{3}} = 7.21 mA
[/Tex]
The cumulative distribution function is given as-
[Tex]
F(x) = \int \limits_{-\infty}^{x} f(x) dx
[/Tex]
There are three regions where the CDF can be defined, [Tex]x<0,\: 0\leq x\leq 25,\:25 < x[/Tex]
[Tex]
\[
F(x) =
\begin{cases}
0, &x<0\\
\frac{x}{25}, &0\leq x\leq 25\\
1, &25<x
\end{cases}
\]
[/Tex]
References –
Probability Distribution – Wikipedia
Uniform Probability Distribution – statelect.com
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