Cluster Analysis: Metrics by Dylan Owen

Explores Bayesian inference, knapsack problem, and prediction using Markov Chain Monte Carlo methods.

Explores clustering in high-dimensional space, covering methods like hierarchical clustering, K-means, and DBSCAN.

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

Explores automated analysis of fluorescence kinetics in microscopy data to reveal protein stoichiometry and its applications.

Covers the Expectation Maximization algorithm and clustering techniques, focusing on Gibbs Sampling and detailed balance.