Neurobiological Signals: Processing and Classification
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Explores neurobiological signal processing, covering spike modeling, signal classification, and data characterization using principal component analysis.
Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
Explores the concept of stationary distribution in Markov chains, discussing its properties and implications, as well as the conditions for positive-recurrence.