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Related lectures (32)

Page 3 of 4

Explores estimation measures of performance, including the Cramér-Rao bound and maximum likelihood estimation.

Covers the elimination of nuisance parameters in statistical inference using Lemmas 14 and 15.

Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.

Covers the estimation of distributions using samples and probability models.

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

Covers the theory behind Gaussian processes and the design of receivers using MAP calculations.

Explores the Kalman filter as a minimal variance estimator and its application in estimating position and velocity.

Explores statistical models, sampling distributions, and hypothesis testing using real-world examples.

Covers regression with exponential family responses using Generalised Linear Models.

Covers estimation of parameters, MSE, Fisher information, and the Rao-Blackwell Theorem.