Bayesian Estimation: Overview and Examples

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Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Generalized Linear Models: A Brief Review

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Estimation Methods in Probability and Statistics

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

Density of States and Bayesian Inference in Computational Mathematics

Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.

Logistic Regression: Statistical Inference and Machine Learning

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

Spin Glasses and Bayesian Estimation

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Maximum Likelihood Theory & Applications

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

Red bus/Blue bus paradox

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Maximum Likelihood: Inference and Model Comparison

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

Bayes Estimator: Definition and Application

Introduces the Bayes estimator, explaining its definition, application in quadratic cost scenarios, and importance in probabilistic reasoning.