...
Detecting Simultaneous Changepoints Across Multiple Data Sequences
Date: April 25, 3pm (NOTE DIFFERENT TIME)
Location: Kings River, 052-306
Speaker: Zhou Fan
Abstract:Â Motivated by applications in genomics, finance, and biomolecular simulation, we introduce a Bayesian model called BASIC for changepoints that tend to co-occur across multiple related data sequences. We design efficient algorithms to infer changepoint locations by sampling from and maximizing over the posterior changepoint distribution. We further develop a Monte Carlo expectation-maximization procedure for estimating unknown prior hyperparameters from data. The resulting framework accommodates a broad range of data and changepoint types, including real-valued sequences with changing mean or variance and sequences of counts or binary observations. We use the resulting BASIC framework to analyze DNA copy number variations in the NCI-60 cancer cell lines and to identify important events that affected the price volatility of S&P 500 stocks from 2000 to 2009.
Optimization for Transportation Efficiency
Date: May 2, 2pm
Speaker: John Fox
Abstract: TBA
Machine Learning at NERSC: Strategy, Tools and Applications
Date: May 16, 2pm
Speaker: Prabhat (NERSC)
...