Lectures related to Latent Space Models for Multiplex Networks

Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Mixture models: taste heterogeneity MOOC: Selected Topics on Discrete Choice

Explores mixture models in discrete choice and random parameters estimation results.

Generative Models: Logistic Regression & Gaussian Distribution

Explores generative models, logistic regression, and Gaussian distribution for approximating posterior probabilities and optimizing model performance.

Mixture models: alternative specific variance MOOC: Selected Topics on Discrete Choice

Explores alternative specific variance in mixture models and discusses identification issues and model comparisons using 500 draws.

Logistic Regression: Model Interpretation and Comparison

Explores logistic regression model interpretation, parameter estimation, and model comparison using likelihood ratio tests.

Horseshoe Crabs: Logistic Regression Analysis

Explores logistic regression analysis of horseshoe crab data, focusing on odds ratio interpretation and model fitting.

Estimation and Confidence Intervals

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Personalized Menu Optimization

Explores Bayesian methods in choice modeling for personalized menu optimization and individual choice prediction.

Intro to Quantum Sensing: Parameter Estimation and Fisher Information

Introduces Fisher Information for parameter estimation based on collected data.

Sampling: conditional maximum likelihood estimation MOOC: Selected Topics on Discrete Choice

Covers Conditional Maximum Likelihood estimation, contribution to likelihood, and MEV model application in choice-based samples.

Logistic Regression: Modeling Binary Response Variables

Explores logistic regression for binary response variables, covering topics such as odds ratio interpretation and model fitting.

Bias, Variance, Consistency, EMV

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

Maximum Likelihood Inference

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

Estimation Methods

Covers various methods for estimating model parameters, such as method of moments and maximum likelihood estimation.

Testing: t-tests

Covers t-tests, p-values calculation, and comparison of coefficients.

MLE for Gaussian: EMV in Gaussian Model

Discusses Maximum Likelihood Estimation for Gaussian mean and variance, exploring parameter estimation in a Gaussian distribution.

Latent Space Models: Inference and Applications Log in to Mediaspace to watch this video

Explores latent space models, network representations, spectral decompositions, and parameter estimation methods.

Maximum Likelihood Theory & Applications Log in to Mediaspace to watch this video

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Linear Models for Classification: Logistic Regression and SVM Log in to Mediaspace to watch this video

Covers linear models for classification, focusing on logistic regression and support vector machines.

Parameter Estimation Log in to Mediaspace to watch this video

Introduces statistical inference concepts, focusing on parameter estimation, unbiased estimators, and mean estimation using independent random variables.