首页>
外国专利>
Efficient gradient computation for conditional Gaussian graphical models
Efficient gradient computation for conditional Gaussian graphical models
展开▼
机译：条件高斯图形模型的有效梯度计算
展开▼
页面导航
摘要
著录项
相似文献
摘要
The subject invention leverages standard probabilistic inference techniques to determine a log-likelihood for a conditional Gaussian graphical model of a data set with at least one continuous variable and with data not observed for at least one of the variables. This provides an efficient means to compute gradients for CG models with continuous variables and incomplete data observations. The subject invention allows gradient-based optimization processes to employ gradients to iteratively adapt parameters of models in order to improve incomplete data log-likelihoods and identify maximum likelihood estimates (MLE) and/or local maxima of the incomplete data log-likelihoods. Conditional Gaussian local gradients along with conditional multinomial local gradients determined by the subject invention can be utilized to facilitate in providing parameter gradients for full conditional Gaussian models.
展开▼
