Lectures related to Parameter Estimation

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Discusses estimation and propagation of uncertainty in random variables and the importance of managing uncertainty in statistical analysis.

Covers statistics, experimental design, errors, distributions, implications of sample size, and null results.

Delves into the binary choice model, error term specification, and Extreme Value distribution properties.

Covers the concept of compression and constructing prefix-free codes based on given information.

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

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

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.