Lectures related to Hyperexponential distribution

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Nonuniform Distributions: Generation Methods

Explores methods for generating nonuniform distributions, including the rejection method of von Neumann and the central limit theorem.

Nonuniform Random Number Distributions: Examples and Methods

Explores nonuniform random number distributions, examples, methods, and generation techniques.

Continuous Time Markov Chains

Introduces continuous time Markov chains on a finite state space with exponential waiting times and jump probabilities.

Continuous Random Variables

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

Poisson processes

Covers the properties and construction of Poisson processes from i.i.d. Exp(X) random variables, emphasizing the importance of the process rate and jump time distributions.

EM Algorithm: Lite-bulbs Lifetime Estimation

Explains the EM algorithm for estimating lite-bulbs lifetime and discusses its application to heavy-bulbs.

Statistical Models: Basics & Applications

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

Maximum Likelihood Estimation

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.

Maximum Likelihood Estimation

Covers Maximum Likelihood Estimation in statistical inference, discussing MLE properties, examples, and uniqueness in exponential families.

Monte-Carlo Integration MOOC: Selected Topics on Discrete Choice

Covers Monte-Carlo integration, simulation, and transforming draws from different distributions.

Logistic Regression: Statistical Inference and Machine Learning

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

Estimation Methods in Probability and Statistics

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

Continuous Random Variables: Distributions and Expectation

Explores various continuous probability distributions and expectation of random variables.

Maximum Likelihood Estimation

Explores Maximum Likelihood Estimation, covering assumptions, properties, distribution, shrinkage estimation, and loss functions.

Common Distributions: Moment Generating Functions

Explores common probability distributions, special distributions, and entropy concepts.

Families of Distributions: PDFs and CDFs

Explores PDFs and CDFs for discrete and continuous random variables, including Gaussian, uniform, and exponential distributions.

Introduction to Continuous Random Variables: Probability Distributions

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

Statistical Estimation Methods

Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.

Continuous Random Variables

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