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Found 3 papers, 1 papers with code
Modeling Function-Valued Processes with Nonseparable and/or Nonstationary Covariance Structure
We discuss a general Bayesian framework on modeling multidimensional function-valued processes by using a Gaussian process or a heavy-tailed process as a prior, enabling us to handle nonseparable and/or nonstationary covariance structure.
Methodology
Active learning for binary classification with variable selection
For a classification problem, this means that the essential label information may not be readily obtainable, in the data set in hands, and an extra labeling procedure is required such that we can have enough label information to be used for constructing a classification model.
Distributed sequential method for analyzing massive data
To analyse a very large data set containing lengthy variables, we adopt a sequential estimation idea and propose a parallel divide-and-conquer method.
