排序方式: 共有6条查询结果,搜索用时 15 毫秒
1
1.
2.
3.
4.
Markov chain Monte Carlo methods explicitly defined on the manifold of probability distributions have recently been established. These methods are constructed from diffusions across the manifold and the solution of the equations describing geodesic flows in the Hamilton–Jacobi representation. This paper takes the differential geometric basis of Markov chain Monte Carlo further by considering methods to simulate from probability distributions that themselves are defined on a manifold, with common examples being classes of distributions describing directional statistics. Proposal mechanisms are developed based on the geodesic flows over the manifolds of support for the distributions, and illustrative examples are provided for the hypersphere and Stiefel manifold of orthonormal matrices. 相似文献
5.
6.
Datasets that are subjectively labeled by a number of experts are becoming more common in tasks such as biological text annotation
where class definitions are necessarily somewhat subjective. Standard classification and regression models are not suited
to multiple labels and typically a pre-processing step (normally assigning the majority class) is performed. We propose Bayesian
models for classification and ordinal regression that naturally incorporate multiple expert opinions in defining predictive
distributions. The models make use of Gaussian process priors, resulting in great flexibility and particular suitability to
text based problems where the number of covariates can be far greater than the number of data instances. We show that using
all labels rather than just the majority improves performance on a recent biological dataset. 相似文献
1