Lectures related to Inference Problems & Spin Glass Game

Explores optimal Bayesian inference, denoising, scalar estimation, and phase transitions.

Bayesian Inference: Precision in Gaussian Model

Explores Bayesian inference for precision in the Gaussian model with known mean, using a Gamma prior and discussing subjective vs objective priors.

Estimation & Bayesian Inference

Covers demousing, estimation, Bayesian inference, likelihood, AWGN, and more.

Maximum Likelihood: Inference and Model Comparison

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

Bayesian Inference: Estimation & Demystification

Covers the concepts of Demystification, Estimation, and Bayesian Inference in the context of Bayesian statistics.

Maximum Likelihood Inference

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

Probability and Statistics

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Dirichlet-Multinomial Model

Discusses the Dirichlet distribution, Bayesian inference, posterior mean and variance, conjugate priors, and predictive distribution in the Dirichlet-Multinomial model.

Bayesian Inference: Beta-Bernoulli Model

Explores the Beta distribution, Bayesian inference, and posterior calculation in the Beta-Bernoulli model.

Bayesian Statistics: Hypothesis Testing and Estimation

Covers hypothesis testing, p-values, significance levels, and Bayesian estimation.

Linear Regression: Statistical Inference Perspective

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

Bayesian Inference: Optimal Decisions

Explores Bayesian inference for optimal decision-making in hypothesis testing scenarios.

Monte Carlo: Markov Chains

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

Probability and Statistics: Independence and Conditional Probability

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

Exponential Family

Covers the properties of the exponential family and the estimation of parameters.

Relevance Vector Machine: Addressing SVM Shortcomings

Introduces the Relevance Vector Machine as a sparse solution to SVM shortcomings.

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Statistical Inference: Exponential Families and Likelihoods

Explores exponential families, likelihood functions, and model regularity in statistical inference.

Statistical Inference

Covers likelihood ratio statistic, confidence intervals, and hypothesis testing concepts.

Bayesian Estimation Log in to Mediaspace to watch this video

Covers the fundamentals of Bayesian estimation, focusing on the application of Bayes' Theorem in scalar estimation.