Covers Markov processes, transition densities, and distribution conditional on information, discussing classification of states and stationary distributions.
Introduces mathematical tools for communication systems and data science, focusing on stochastic processes and preparing students for advanced courses.
Explores communicating classes in Markov chains, distinguishing between transient and recurrent classes, and delves into the properties of these classes.
Explores neurobiological signal processing, covering spike modeling, signal classification, and data characterization using principal component analysis.