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probability

Random variables

taught

Taught in

Requires

Required by

Local knowledge map

Local knowledge map

Covariance From mass to density Joint probability density functions Joint probability mass functions Linear transformations, Y=aX+b Mean under a linear transformation Simulating the sampling distribution of a mean Sums of independent variables The Bernoulli distribution The Gaussian-noise / squared-loss connection The cumulative distribution function The indicator trick The sample average as a random variable What makes a pmf valid A statistic is a random variable, revisited Discrete versus continuous: a first look Functions of a random variable Indicator random variables Linearity of expectation The probability mass function Two random variables at once Random variables Expected value Expected value of a function of X (LOTUS) Parameter vs. statistic Probability as a measure Probability rules Counting-based probability Sample spaces and events

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Solid arrows are required prerequisites; dashed are recommended or helpful. Lower rows are more foundational. Click any box to jump to that concept.