Statistical Inference: Random Variables

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (30)

Page 3 of 3

Statistical Theory: Fundamentals

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Causal Systems & Transforms: Delay Operator Interpretation

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.

Probability Distributions: Basics and Properties

Covers the basics of probability distributions, including mean, variance, and properties of random variables.

Continuous Random Variables: Basics

Explores continuous random variables, cdf, pdf, convolution, and sum of dice.

Elements of Statistics

Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.

Fundamental Limits of Gradient-Based Learning

Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.

Probability Theory: Basics and Applications

Covers the fundamentals of probability theory, including corollaries, conditional probability, total probability theorem, and random variables.

Conditional Density and Expectation

Covers conditional density, independence of random variables, expectation, and variance calculation.

Probability Theory: Random Variables and Independence

Explores discrete and continuous random variables, independence, and probability functions.

Probability and Statistics

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