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Related lectures (32)

Statistical Physics of Clusters

Explores the statistical physics of clusters, focusing on complexity and equilibrium behavior.

Bayesian Networks: Fundamentals and Applications

Covers the fundamentals of Bayesian Networks and their applications in probabilistic topic modeling.

Personalized Menu Optimization

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

Robust State Estimation: ROSE

Discusses the ROSE framework for robust state estimation and multi-modal sensor fusion.

Uncertain Reasoning: Bayesian Networks

Explores uncertain reasoning, Bayesian networks, and stochastic resolution, emphasizing the importance of probabilistic logic and abduction.

Inference for Stochastic Processes: Large Networks Analysis

Explores inference for stochastic processes, emphasizing large networks analysis and the need for new theories and methods.

Gaussian Acyclic Models: Linearity and Identifiability

Covers Gaussian Acyclic Models focusing on linearity and identifiability.

Event Analysis: Accident Causal Tree

Explores accident analysis through the causal tree method and real-life examples.

Variational Autoencoders

Covers Variational Autoencoders, a probabilistic approach to autoencoders for data generation and feature representation, with applications in Natural Language Processing.

Bayesian Networks: Factorization and Sampling

Explains Bayesian Networks factorization and sampling methods using DAGs and Variable Elimination.

Optimization and Simulation: Bayesian Inference

Explores Bayesian inference, knapsack problem, and prediction using Markov Chain Monte Carlo methods.

Bayesian Parameter Estimation

Covers an example of Bayesian parameter estimation and the trade-off between bias and variance in supervised learning.

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