Bayesian Estimation

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Related lectures (30)

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Bayesian Inference: Optimal Estimation

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

Estimation & Bayesian Inference

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

Bayesian Inference: Estimation & Demystification

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

Distribution Estimation

Covers the estimation of distributions using samples and probability models.

Distribution Estimation

Covers the estimation of distributions using various methods such as minimum loss and expectation.

Inference Problems & Spin Glass Game

Covers inference problems related to the Spin Glass Game and the challenges of making mistakes with preb P.

Generative Models: Logistic Regression & Gaussian Distribution

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

Monte Carlo: Markov Chains

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

Bayesian Inference: Gaussian Prior for Mean

Discusses Bayesian inference for the mean of a Gaussian distribution with known variance, covering posterior mean, variance, and MAP estimator.

Estimation and Confidence Intervals

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