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Multinomial Distribution

The multinomial distribution is an extension of the binomial distribution, used when there are more than two possible outcomes. Examples include dice rolls or survey results where respondents select from multiple options.

Key Topics

Generating Multinomial Distribution

NumPy's random.multinomial() function generates random outcomes based on specified probabilities for multiple categories.

Example

# Generating multinomial distribution
import numpy as np

# Rolling a dice 10 times
outcomes = np.random.multinomial(10, [1/6]*6, size=1)

print("Dice roll outcomes:", outcomes)

Output

Dice roll outcomes: [[2 1 3 0 2 2]]

Explanation: The random.multinomial() function simulates rolling a six-sided dice 10 times. Each number (1-6) has an equal probability of 1/6.

Key Takeaways

  • Multinomial Distribution: Extends binomial distribution for multiple outcomes.
  • Simulation: Use random.multinomial() to simulate events like dice rolls or survey results.
  • Applications: Model scenarios with more than two possible outcomes, such as market share analysis or voting patterns.
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