Latent and observable variables

Applied sciences
Information engineering
Data science
Graphical models

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Choice models with latent variables: Modeling latent concepts

Explores choice models with latent variables and their estimation process based on likelihood integration.

Knowledge Tracing: Student Learning and Latent Variables

Explores Bayesian Knowledge Tracing and latent variables for tracing student knowledge and making predictions.

Learning Latent Models in Graphical Structures

Explores learning latent models in graphical structures, focusing on scenarios with incomplete samples and introducing the notion of distance among variables.

Topic Models: Understanding Latent Structures

Explores topic models, Gaussian mixture models, Latent Dirichlet Allocation, and variational inference in understanding latent structures within data.

Statistical Analysis of Networks: Link Prediction and Biclustering

Explores link prediction, logistic regression, causal inference, and biclustering in statistical network analysis.

Latent Variable Models

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

Introduction to ML for Behavioral Data

Covers machine learning approaches for personalization and their real-world application, emphasizing homework, projects, and feedback.

Recent Advances in Structural Learning for Graphical Models

Covers recent advances in structural learning for graphical models, including Gaussian models, mixed models, and extreme events.

Front Door Criterion: Adjustment Formula

Explores the front door criterion for valid adjustment sets in causal inference.

Interactive Lecture HMM: Definitions and Topologies

Explores Hidden Markov Models definitions, topologies, learning process, and current research trends.