- Probability - Basic Terminology
- Two Defining Properties of Probability
- Empirical, Subjective and Priori Probability
- State the Probability of an Event as Odds
- Unconditional and Conditional Probabilities
- Multiplication, Addition and Total Probability Rules
- Joint Probability of Two Events
- Probability of Atleast One of the Events Occuring
- Dependent Vs. Independent Events in Probability
- Joint Probability of a Number of Independent Events
- Unconditional Probability Using Total Probability Rule
- Expected Value of Investments
- Calculating Variance and Standard Deviation of Stock Returns
- Conditional Expected Values
- Calculating Covariance and Correlation
- Expected Value of a Portfolio
- Variance and Standard Deviation of a Portfolio
- Bayes’ Theorem
- Multiplication Rule of Counting
- Permutation and Combination Formula

# Joint Probability of a Number of Independent Events

If two events are independent, then the joint probability of these two independent events is calculated as:

**P(A and B) = P(A) x P(B)**

**Example**

Suppose we roll two dice. The joint probability of getting a 1 on first die and a 6 on the second die is given as follows:

Probability of getting a 1 on first die, P(A) = 1/6

Probability of getting a 6 on second die, P(B) = 1/6

**P(A and B) = 1/6 * 1/6 = 1/36 = 0.0278**

The same rule can be applied to calculate the joint probability of any number of independent events. For example, if there are three independent events, A, B and C, their joint probability will be:

**P(A and B and C) = P(A) x P(B) x P(C)**

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