Lectures related to Special Families of Models

Exponential Family: Maximum Entropy Distributions

Covers exponential families and maximum entropy distributions under moment constraints.

Statistical Models: Basics & Applications

Covers statistical concepts like probability, estimation, hypothesis testing, and confidence intervals for mathematicians.

Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.

Generalized Linear Models

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Sufficient Statistics: Understanding Data Compression

Explores sufficient statistics, data compression, and their role in statistical inference, with examples like Bernoulli Trials and exponential families.

Continuous Random Variables

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Sampling Distributions: Theory and Applications

Explores sampling distributions, estimators' properties, and statistical measures for data science applications.

Probability and Statistics

Covers probability distributions, moments, and continuous random variables.

Exponential Family

Covers the properties of the exponential family and the estimation of parameters.

Advanced Probabilities: Random Variables & Expected Values

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Introduction to Continuous Random Variables: Probability Distributions

Introduces continuous random variables and their probability distributions, emphasizing their applications in statistics and data science.

Continuous Random Variables

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

Estimation Methods in Probability and Statistics

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Linear Combinations: Moment-Generating Functions

Explores moment-generating functions, linear combinations, and normality of random variables.

Maximum Likelihood Estimation: Multivariate Statistics

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Confidence Intervals: Definition and Estimation

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Probability and Statistics

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Probability Distributions in Environmental Studies Log in to Mediaspace to watch this video

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential 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.

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.