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Lesson 4 of 20
Quiz

Discrete Uniform Random Variable

A discrete uniform random variable is a discrete random variable for which the probability of each outcome is the same.

Example

The roll of a die is a discrete uniform random variable and has a discrete uniform probability distribution.

The random variable X can take the values X = {1, 2, 3, 4, 5, 6}

Each outcome has a probability of 1/6.

The probability distribution and cumulative distribution functions are shown below:

xiProbability distribution, P(xi)Cumulative Distribution, F(xi)
11/61/6 = 0.1667
21/62/6 = 0.333
31/63/6 = 0.5
41/64/6 = 0.667
51/65/6 = 0.833
61/66/6 = 1
dpd
dpd

Let’s observe a few values from the above table.

1P(3) = 1/6 or 0.1667
2
3F(3) = 0.5
4
5P(2<=X<=5) = 4\*1/6 = 0.667
6

We can generalize this as follows:

1P(x) = 1/6 or 0.1667
2
3Cumulative distribution function for any outcome i is F(xi) = i\*P(x)
4
5Probability function for a range with k outcomes = k\*P(x)
6

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Cumulative Distribution Function

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Bernoulli and Binomial Distribution

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Common Probability Distributions

20 lessons

Lessons

1
What is a Probability Distribution
2
Discrete Vs. Continuous Random Variable
3
Cumulative Distribution Function
4
Discrete Uniform Random Variable
5
Bernoulli and Binomial Distribution
6
Stock Price Movement Using a Binomial Tree
7
Tracking Error and Tracking Risk
8
Continuous Uniform Distribution
9
Normal Distribution
10
Univariate Vs. Multivariate Distribution
11
Confidence Intervals for a Normal Distribution
12
Standard Normal Distribution
13
Calculating Probabilities Using Standard Normal Distribution
14
Shortfall Risk
15
Safety-first Ratio
16
Lognormal Distribution and Stock Prices
17
Discretely Compounded Rate of Return
18
Continuously Compounded Rate of Return
19
Option Pricing Using Monte Carlo Simulation
20
Historical Simulation Vs Monte Carlo Simulation

Quizzes

Common Probablity Distributions
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