Bayesian Estimation

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Related lectures (30)

Bayesian Inference: Beta-Bernoulli Model

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

Sampling Distributions: Theory and Applications

Explores sampling distributions, estimators' properties, and statistical measures for data science applications.

Concentration Inequalities

Covers concentration inequalities and sampling methods for estimating unknown distributions, with a focus on population infection rates.

Continuous Random Variables: Basic Ideas

Explores continuous random variables and their properties, including support and cumulative distribution functions.

Nonparametric and Bayesian Statistics

Covers nonparametric statistics, kernel density estimation, Bayesian principles, and posterior distribution summarization.

Confidence Intervals: Definition and Estimation

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Probabilities and Statistics

Covers fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.

Elements of Statistics: Probability, Distributions, and Estimation Log in to Mediaspace to watch this video

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Spin Glasses and Bayesian Estimation Log in to Mediaspace to watch this video

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

Bayesian Extremes: Markov Chain Monte Carlo Log in to Mediaspace to watch this video

Explores Bayesian techniques for extreme value problems, including Markov Chain Monte Carlo and Bayesian inference, emphasizing the importance of prior information and the use of graphs.

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