Covers the Markov Chain Monte Carlo method and the Metropolis-Hastings algorithm for generating samples from a target probability distribution.
Covers error estimation in Latin Hypercube Sampling, emphasizing the importance of accurate variance estimation.
Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.
Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.
Explores the Stein Phenomenon, showcasing the benefits of bias in high-dimensional statistics and the superiority of the James-Stein Estimator over the Maximum Likelihood Estimator.
