MATH-232: Probability and statistics (for IC)

Lectures in this course (61)

Covers conditional probability distributions and introduces the concept of conditional expected value.

Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.

Covers probability distributions, moments, and continuous random variables.

Covers properties and calculations related to the normal distribution, including probabilities and quantiles.

Explores continuous random variables and their properties, including support and cumulative distribution functions.

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Covers probability, statistics, independence, covariance, correlation, and random variables.

Covers various probability distributions and their applications in real-world scenarios.

Covers the multinomial distribution, joint density, marginal distribution, and conditional distribution.

Explores generating functions, Laplace transform, and their role in probability distributions.

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

Explores moment-generating functions, linear combinations, and normality of random variables.

Explores independence and covariance between random variables, discussing their implications and calculation methods.

Explores moment generating functions and multivariate normal distributions in probability and statistics.

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