Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Probability Distributions: Moments and Transformations
Graph Chatbot
Related lectures (59)
Linear Combinations: Moment-Generating Functions
Explores moment-generating functions, linear combinations, and normality of random variables.
Continuous Random Variables
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
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.
All of probability: generating functionals and cumulants
Covers moment generating function, cumulants, Laplace transform, and Fourier transform in probability theory.
Central Limit Theorem
Explores the Central Limit Theorem, convergence in law, characteristic functions, and moment problems in probability theory.
Moment Generating Function and Multivariate Normal Distribution
Explores moment generating functions and multivariate normal distributions in probability and statistics.
Generating Functions: Properties and Applications
Explores generating functions, Laplace transform, and their role in probability distributions.
Random Variables: Expectation and Independence
Explores random variables, expectation, and independence in probability theory.
Continuous Random Variables: Basic Ideas
Explores continuous random variables and their properties, including support and cumulative distribution functions.
Normal Distribution: Properties and Calculations
Covers properties and calculations related to the normal distribution, including probabilities and quantiles.
Probability and Statistics
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Statistics for Data Science: Basics and Modelling
Introduces statistical modelling basics, probability theory, and key concepts for data science applications.
Conditional Density and Expectation
Covers conditional density, independence of random variables, expectation, and variance calculation.
Probability Theory: A Review
Offers a review of key probability concepts, including probability spaces, events, and random variables.
Probability Theory: Midterm Solutions
Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.
Continuous Random Variables
Covers continuous random variables, probability density functions, and distributions, with practical examples.
Probability and Statistics
Log in to Mediaspace to watch this video
Explores joint random variables, conditional density, and independence in probability and statistics.
Causal Systems & Transforms: Delay Operator Interpretation
Log in to Mediaspace to watch this video
Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.
Statistical Theory: Fundamentals
Log in to Mediaspace to watch this video
Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.
Properties of Distribution Functions
Log in to Mediaspace to watch this video
Covers the properties of distribution functions, discrete and continuous random variables, independence, and notation in probability theory.
Previous
Page 1 of 3
Next
