Lectures related to End of the M-step derivation

Likelihood Ratio Test: Detection & Estimation

Covers the likelihood ratio test for detection and estimation in statistical analysis.

Likelihood Ratio Test: Hypothesis Testing

Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.

Linear Algebra: Matrices and Operations

Introduces key concepts in linear algebra, including matrices, operations, and numerical invariants.

Likelihood of a spike train MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Discusses the likelihood of spike trains based on generative models and log-likelihood calculations from observed data.

Maximum Likelihood Estimation: Multivariate Statistics

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

Maximum Likelihood Inference

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Linear Regression: Statistical Inference Perspective

Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.

Maximum Likelihood: Inference and Model Comparison

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Binary Covariate Impact: 2x2 Contingency Tables

Explores the impact of a binary covariate on binary responses using 2x2 tables.

Latent Variable Models

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.

Exponential Family: Properties and Estimation

Explores exponential families, Bernoulli distributions, parameter estimation, and maximum entropy distributions in statistical modeling.

The Likelihood of Data under a Model

Explores the likelihood of data under a model and the concept of maximum likelihood.

Linear Algebra Review

Covers the basics of linear algebra, including matrix operations and singular value decomposition.

Likelihood Ratio Test MOOC: Introduction to Discrete Choice Models

Covers the Likelihood Ratio Test in choice models, comparing unrestricted and restricted models through benchmarking and testing different model specifications.

Matrix Representations of Linear Applications

Covers the matrix representations of linear applications and the equivalence between matrices.

Linear Algebra in 3D: Determinants and Inverses

Explores determinants in 3D space and their role in linear algebra.

Testing: Likelihood Ratio Test

Covers the likelihood ratio test in choice models, benchmarking, and tests for taste variations and nonlinear specifications.

Linear Transformations: Matrices and Kernels

Covers linear transformations, matrices, kernels, and properties of invertible matrices.

Statistical Interpretation of Artificial Neural Networks

Delves into the statistical interpretation of artificial neural networks, exploring the likelihood of data and maximizing model accuracy.

Logistic Regression: Statistical Inference and Machine Learning

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