Introduction to Probability | Electrical Engineering and Computer Science | MIT OpenCourseWare

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Electrical Engineering and Computer Science

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Spring 2018

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Undergraduate

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Lecture Notes

Introduction to Probability, Selected Textbook Summary Material

Lecture 1 Supplement: Mathematical Background

Lecture 1: Probability Models and Axioms

Lecture 1: Probability Models and Axioms

Lecture 2: Conditioning and Bayes' Rule

Lecture 2: Conditioning and Bayes' Rule

Lecture 3: Independence

Lecture 3: Independence

Lecture 4: Counting

Lecture 4: Counting

Lecture 5: Discrete Random Variables Part I

Lecture 5: Discrete Random Variables Part I

Lecture 6: Discrete Random Variables Part II

Lecture 6: Discrete Random Variables Part II

Lecture 7: Discrete Random Variables Part III

Lecture 7: Discrete Random Variables Part III

Lecture 8: Continuous Random Variables Part I

Lecture 8: Continuous Random Variables Part I

Lecture 9: Continuous Random Variables Part II

Lecture 9: Continuous Random Variables Part II

Lecture 10: Continuous Random Variables Part III

Lecture 10: Continuous Random Variables Part III

Lecture 11: Derived Distributions

Lecture 11: Derived Distributions

Lecture 12: Sum of Independent R.V.s. Covariance and Correlation

Lecture 12: Sum of Independent R.V.s. Covariance and Correlation

Lecture 13: Conditional Expectation & Variance Revisited; Sum of a Random Number of Independent R.V.s

Lecture 13: Conditional Expectation & Variance Revisited; Sum of a Random Number of Independent R.V.s

Lecture 14: Introduction to Bayesian Inference

Lecture 14: Introduction to Bayesian Inference

Lecture 15: Linear Models With Normal Noise

Lecture 15: Linear Models With Normal Noise

Lecture 16: Least Mean Squares (LMS) Estimation

Lecture 16: Least Mean Squares (LMS) Estimation

Lecture 17: Linear Least Mean Squares (LLMS) Estimation

Lecture 17: Linear Least Mean Squares (LLMS) Estimation

Lecture 18: Inequalities, Convergence, and the Weak Law of Large Numbers

Lecture 18: Inequalities, Convergence, and the Weak Law of Large Numbers

Lecture 19: The Central Limit Theorem (CLT)

Lecture 19: The Central Limit Theorem (CLT)

Lecture 20: An Introduction to Classical Statistics

Lecture 20: An Introduction to Classical Statistics

Lecture 21: The Bernoulli Process

Lecture 21: The Bernoulli Process

Lecture 22: The Poisson Process Part I

Lecture 22: The Poisson Process Part I

Lecture 23: The Poisson Process Part II

Lecture 23: The Poisson Process Part II

Lecture 24: Finite-State Markov Chains

Lecture 24: Finite-State Markov Chains

Lecture 25: Steady-State Behavior of Markov Chains

Lecture 25: Steady-State Behavior of Markov Chains

Lecture 26: Absorption Probabilities and Expected Time to Absorption

Lecture 26: Absorption Probabilities and Expected Time to Absorption

Course Info

Instructors

Departments

Electrical Engineering and Computer Science

As Taught In

Spring 2018

Level

Undergraduate

Topics

Learning Resource Types

Lecture Notes

Lecture Videos

Download Course

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