Common Discrete Distributions

Learn common discrete distributions.

26 min read

Intermediate

Introduction

Learning Objectives:

Master Bernoulli, Binomial, Geometric, Poisson distributions
Know when to apply each distribution
Compute probabilities with scipy.stats

Binomial Distribution

$X sim ext{Binomial}(n, p)$ : number of successes in $n$ independent Bernoulli $(p)$ trials.

$P(X = k) = inom{n}{k} p^k (1-p)^{n-k}$

Mean: $np$ , Variance: $np(1-p)$

Key Concepts

This lesson covers fundamental concepts in probability theory.

Key Takeaways

Master these concepts for advanced probability applications.

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Combinatorics and Probability: From Counting to Inference

Master the mathematical foundations of counting, probability, and randomness. This comprehensive course bridges pure combinatorics with computational probability, covering everything from basic counting principles to advanced probabilistic methods, limit theorems, and real-world applications in algorithms, finance, and data science.

IntroductionFocusStart Focus Mode

Binomial Distribution

Key ConceptsFocusStart Focus Mode

Key TakeawaysFocusStart Focus Mode

Introduction

Key Concepts

Key Takeaways