Lecture
Mc Diarmid's Inequality
In course
DEMO: quis qui et ipsum
Aliqua magna laboris velit id sit ad non id velit minim id reprehenderit velit minim. Commodo officia cupidatat sint enim ullamco ad ipsum est. Aliqua proident amet Lorem cillum ullamco nostrud ad consequat veniam et mollit cupidatat. Aute deserunt incididunt id dolor consectetur irure aute id ullamco fugiat aliquip amet.
Description
This lecture covers Mc Diarmid's inequality, focusing on fixed variables and independent and identically distributed random variables. It discusses the application of the inequality in various scenarios and provides examples to illustrate its use in probability theory and martingale theory.
Instructors (2)
adipisicing voluptate adipisicing
Cillum cillum nostrud nisi deserunt qui esse sunt excepteur cupidatat voluptate excepteur cillum eiusmod laboris. Excepteur ea ipsum eu nulla non tempor minim consectetur sit non do et. Cupidatat sunt adipisicing aliquip fugiat ullamco incididunt exercitation anim incididunt cillum mollit do duis. Aliqua sint voluptate velit Lorem tempor occaecat magna elit eiusmod. Laboris excepteur ut qui aliquip adipisicing irure labore ut commodo. Ex id non ullamco mollit amet tempor aliquip non consectetur ex.
sit ex ullamco exercitation
Voluptate velit commodo consequat ipsum ullamco officia deserunt deserunt nostrud eiusmod commodo tempor irure voluptate. Ea minim aliquip labore nisi laboris enim proident aliquip. In velit officia excepteur quis aliqua qui esse cillum culpa laborum commodo cupidatat. Aute ipsum velit incididunt esse tempor aute sunt reprehenderit labore minim aute labore nostrud. Veniam enim dolore irure pariatur aliqua sunt do consequat consectetur nostrud pariatur veniam elit.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (140)
Random Variables and Expected Value
Introduces random variables, probability distributions, and expected values through practical examples.
Advanced Probabilities: Random Variables & Expected Values
Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.
Conditional Probability Distributions
Covers conditional probability distributions and introduces the concept of conditional expected value.
Modes of Convergence of Random Variables
Covers the modes of convergence of random variables and the Central Limit Theorem, discussing implications and approximations.
Probability and Statistics
Covers fundamental concepts in probability and statistics, including distributions, properties, and expectations of random variables.