Concept

Marginal distribution

Related lectures (28)

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

Covers the transformations of joint continuous densities and their implications on probability distributions.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Gaussian Mixture Models: Exercises

Covers exercises on GMM, focusing on conditional and marginal distributions.

Insurance Risk Modeling

Covers the modeling of insurance risk, discussing joint distributions and the impact of accidents.

Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Polymer Theory: End-to-End Distance

Explores the calculation of end-to-end distance in polymer theory, focusing on isotropy, probability distributions, and the Kratky-Porod model.

Continuous Random Variables

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Multivariable Control

Covers Gaussian random variables, affine transformations, and linear systems driven by Gaussian noise in multivariable control.

Multivariate Statistics: Normal Distribution

Introduces multivariate statistics, covering normal distribution properties and characteristic functions.

Bayesian Inference: Gaussian Variables

Explores Bayesian inference for Gaussian random variables, covering joint distribution, marginal pdfs, and the Bayes classifier.

Probability Distributions: Discrete and Continuous

Covers discrete and continuous probability distributions, including joint and conditional probabilities.

Random Variables and Information Theory Concepts

Introduces random variables and their significance in information theory, covering concepts like expected value and Shannon's entropy.

Probability and Statistics: Fundamental Theorems

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

All of Probability: Notations and PDF

Covers the fundamentals of probability theory, including notations for joint and conditional distributions.

Markov Chains: Transition Densities

Covers Markov processes, transition densities, and distribution conditional on information, discussing classification of states and stationary distributions.

Probability and Statistics

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

Statistical Consequences of Clustering

Covers the statistical consequences of clustering and the complexities of return level estimation in extreme events.

Probability Distributions

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

Introduction to Inference

Covers the basics of probability theory, random variables, joint probability, and inference.