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Explore Bayesian nonparametric approaches for modeling complex dynamical phenomena in this lecture by Emily Fox from the University of Pennsylvania. Delve into the development of methods that allow data to define the complexity of inferred classes of models while enabling efficient computational algorithms for inference. Learn about the use of stochastic processes like beta and Dirichlet processes to define priors on an unbounded number of potential Markov models. Discover applications of these techniques in speaker diarization, stock market volatility analysis, honeybee dance studies, and human motion capture videos. Gain insights into the speaker's research interests in multivariate time series analysis and Bayesian nonparametric methods, as well as her academic background and achievements in the field of electrical engineering and computer science.
