Lectures related to Variance function

Generalized Linear Models

Introduces Generalized Linear Models, showcasing examples with different distributions and real-world data.

Estimating R: Moments of a Distribution

Explains the importance of moments in measuring distribution properties, such as expectation and variance.

Estimating Deviations

Covers Markov's and Chebyshev's Inequalities for random variables and probability distributions.

Stochastic Processes: Symmetric Random Walk

Covers the properties of the symmetric random walk in stochastic processes.

Conditional Expectation and Variance

Explores conditional expectation and variance, showing how variables influence each other's probability distribution.

Variance and Covariance: Properties and Examples

Explores variance, covariance, and practical applications in statistics and probability.

Sampling a Probability Distribution

Explores sampling a probability distribution and structure functions in statistical analysis.

Max Entropy and Monte Carlo

Explores max entropy, Shannon's entropy, Lagrange multipliers, and Monte Carlo sampling techniques.

Optimization and Simulation

Covers the Metropolis-Hastings algorithm and gradient-based approaches for biasing searches towards higher likelihood values.

Poisson Process: Properties

Covers the properties of Poisson processes, including arrival times and stochastic models for communications.

Bias, Variance, Consistency, EMV

Covers bias, variance, mean squared error, consistency, and maximum likelihood estimation in the Poisson model.

Modern Regression: Overdispersion and Model Assessment

Explores overdispersion, model assessment, and regression techniques for count data.

Common Distributions: Moments and MGFs

Covers common distributions, moment generating functions, and covariance matrices in statistics for data science.

Probability Distributions: Basics and Properties

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

Probability and Statistics: Fundamental Theorems

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

Probability Distributions: Moments and Quantiles

Explains moments, expectation, variance, and quantiles in probability distributions with examples like Poisson and exponential distributions.

Stochastic Models for Communications

Explores mean, variance, probability functions, inequalities, and various types of random variables, including Binomial, Geometric, Poisson, and Gaussian distributions.

Reinforcement Learning for Pacman

Explores applying reinforcement learning to teach Pacman to play autonomously using policy gradient methods and Markov decision processes.

Probabilities and Statistics: Key Theorems and Applications

Discusses key statistical concepts, including sampling dangers, inequalities, and the Central Limit Theorem, with practical examples and applications.

Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.