Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Home

Lecture

Bayesian Inference: Gaussian Variables

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (54)

Normal Distribution: Properties and Calculations Log in to Mediaspace to watch this video

Covers the normal distribution, including its properties and calculations.

Elements of Statistics Log in to Mediaspace to watch this video

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

Estimators and Confidence Intervals Log in to Mediaspace to watch this video

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Unsupervised Learning: PCA & K-means Log in to Mediaspace to watch this video

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Introduction to Inference Log in to Mediaspace to watch this video

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

Stochastic Models for Communications Log in to Mediaspace to watch this video

Covers random vectors, joint probability density, independent random variables, functions of two random variables, and Gaussian random variables.

Estimating R: Marginal and Conditional Distributions Log in to Mediaspace to watch this video

Covers the estimation of R using bivariate normal distributions and explores the marginal and conditional distributions of X₁ and X₂.

Elements of Statistics: Probability and Random Variables Log in to Mediaspace to watch this video

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Statistical Estimators Log in to Mediaspace to watch this video

Explains statistical estimators for random variables and Gaussian distributions, focusing on error functions for integration.

Gaussian Mixture Models & Noisy Signals Log in to Mediaspace to watch this video

Explores Gaussian mixture models and denoising noisy signals using a probabilistic approach.

Eigenstate Thermalization Hypothesis Log in to Mediaspace to watch this video

Explores the Eigenstate Thermalization Hypothesis in quantum systems, emphasizing the random matrix theory and the behavior of observables in thermal equilibrium.

Conditional Probability: Understanding Events and Their Relationships Log in to Mediaspace to watch this video

Covers conditional probability, illustrating how events influence each other's probabilities and its applications in real-world scenarios.

Expectation Maximization: Learning Parameters Log in to Mediaspace to watch this video

Covers the Expectation Maximization algorithm for learning parameters and dealing with unknown variables.

Probability Fundamentals Log in to Mediaspace to watch this video

Introduces fundamental probability concepts, including events, complements, conditional probability, and random variables.

Conditional Probability: Definition and Examples Log in to Mediaspace to watch this video

Explains the calculation of conditional probability with definitions and examples.

Review Session: Module 1 Log in to Mediaspace to watch this video

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.

Central Limit Theorem Log in to Mediaspace to watch this video

Covers the central limit theorem, showing how random processes converge to a normal distribution.

Law of Large Numbers: Strong Convergence Log in to Mediaspace to watch this video

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Statistical Models and Parameter Estimation Log in to Mediaspace to watch this video

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Statistical Theory: Maximum Likelihood Estimation Log in to Mediaspace to watch this video

Explores the consistency and asymptotic properties of the Maximum Likelihood Estimator, including challenges in proving its consistency and constructing MLE-like estimators.

Page 2 of 3