Bayesian Nonparametrics for Signal Processing
Presented by: Michael I. Jordan (Department of EECS, UC Berkeley, USA), Author(s): Michael I. Jordan (Department of EECS, UC Berkeley, USA)
Bayesian nonparametric statistics involves replacing the "prior distributions" of classical Bayesian analysis with "prior stochastic processes." Of particular value are the class of "combinatorial stochastic processes," which make it possible to express uncertainty (and perform inference) over structural aspects of models, including cardinalities, couplings and segmentations. This has allowed upgrades to many classical probabilistic models, including hidden Markov models, mixture models and tree-based models, and it has allowed the design of entirely new models. I overview some of the basics of combinatorial stochastic processes and present applications to several problems in and around speech and language technology, including speaker diarization and multiple time series segmentation.