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The probability_cheatsheet is a cheat sheet repository that summarizes key probability theory concepts, formulas, distributions, and properties in a concise format. It likely includes definitions of random variables, PMFs and PDFs, expectations, variance, common distributions (e.g. binomial, normal, Poisson, exponential), conditional probability, Bayes’ theorem, moment generating functions, and perhaps important inequalities (Markov, Chebyshev, Chernoff). The cheat sheet is intended as a quick reference for students, data scientists, statisticians, or anyone needing to recall core probability formulas without diving into textbooks. It may include visual diagrams (e.g. distributions’ shapes), tips or mnemonic notes, and examples of application (e.g. computing probabilities or expectations). Formats could include Markdown, PDF, or images for easy inclusion in study materials or slides.
Features
- Condensed definitions and formulas for random variables, expectation, variance
- Key distributions (normal, binomial, Poisson, exponential) with their properties
- Conditional probability rules, Bayes’ theorem, and independence concepts
- Important inequalities and bounds (e.g. Markov, Chebyshev)
- Visual aids or diagrams illustrating distribution shapes or relationships
- Portable formats (Markdown, PDF, image) for reference or inclusion in notes
Project Samples
