(1.4 hours to learn)
Intuitively, two events are independent if the first event happening doesn't influence whether the second is likely to occur. Mathematically, some set of events are independent if the joint probability of some subset of the events decomposes into a product of the probabilities of the individual events. In statistics and AI, a probabilistic model often must make independence assumptions in order for things to be efficiently computable.
This concept has the prerequisites:
Core resources (read/watch one of the following)
→ Mathematical Monk: Probability Primer (2011)
→ Mathematical Statistics and Data Analysis
An undergraduate statistics textbook.
Location: Section 1.6, "Independence," pages 23-26
→ An Introduction to Probability Theory and its Applications
A classic introductory probability textbook.
Location: Section 5.3, "Stochastic independence," pages 114-117
→ Probability and Statistics
An introductory textbook on probability theory and statistics.
Location: Section 2.2, "Independent events," pages 56-64
→ A First Course in Probability
An introductory probability textbook.
Location: Section 3.4, "Independent events," pages 87-101
Supplemental resources (the following are optional, but you may find them useful)
→ BerkeleyX: Introduction to Statistics: Probability
An online course on basic probability.
Location: Lecture 2.1, "Independence"
→ Sets, Counting, and Probability
Online lectures on basic probability theory.
Location: Lecture sequence "Conditional probability"
→ Khan Academy: Probability and Statistics
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