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作者：Percival, Daniel; Roeder, Kathryn; Rosenfeld, Roni; Wasserman, Larry
作者单位：Carnegie Mellon University; Carnegie Mellon University
摘要：We introduce a new version of forward stepwise regression. Our modification finds solutions to regression problems where the selected predictors appear in a structured pattern, with respect to a predefined distance measure over the candidate predictors. Our method is motivated by the problem of predicting HIV-1 drug resistance from protein sequences. We find that our method improves the interpretability of drug resistance while producing comparable predictive accuracy to standard methods. We a...
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作者：Wang, Yazhen
作者单位：University of Wisconsin System; University of Wisconsin Madison
摘要：Contemporary scientific studies often rely on the understanding of complex quantum systems via computer simulation. This paper initiates the statistical study of quantum simulation and proposes a Monte Carlo method for estimating analytically intractable quantities. We derive the bias and variance for the proposed Monte Carlo quantum simulation estimator and establish the asymptotic theory for the estimator. The theory is used to design a computational scheme for minimizing the mean square err...
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作者：Fox, Emily B.; Sudderth, Erik B.; Jordan, Michael I.; Willsky, Alan S.
作者单位：Duke University; University of California System; University of California Berkeley; University of California System; University of California Berkeley; Brown University; Massachusetts Institute of Technology (MIT)
摘要：We consider the problem of speaker diarization, the problem of segmenting an audio recording of a meeting into temporal segments corresponding to individual speakers. The problem is rendered particularly difficult by the fact that we are not allowed to assume knowledge of the number of people participating in the meeting. To address this problem, we take a Bayesian nonparametric approach to speaker diarization that builds on the hierarchical Dirichlet process hidden Markov model (HDP-HMM) of T...
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作者：Speed, Doug; Tavare, Simon
作者单位：University of Cambridge; University of Cambridge
摘要：This paper presents Sparse Partitioning, a Bayesian method for identifying predictors that either individually or in combination with others affect a response variable. The method is designed for regression problems involving binary or tertiary predictors and allows the number of predictors to exceed the size of the sample, two properties which make it well suited for association studies. Sparse Partitioning differs from other regression methods by placing no restrictions on how the predictors...
