论文标题
正常和蒙特卡洛分布的最小相对熵推断
Minimum Relative Entropy Inference for Normal and Monte Carlo Distributions
论文作者
论文摘要
我们代表指数家庭分布的仿射子序列，作为最小相对熵子字节。通过这样的表示，我们得出了分析公式的推论，从有关多元正常分布的期望和协方差的部分信息。我们通过蒙特卡洛模拟改善了数值实现，从而从广义期望类型的部分信息进行了推断。
We represent affine sub-manifolds of exponential family distributions as minimum relative entropy sub-manifolds. With such representation we derive analytical formulas for the inference from partial information on expectations and covariances of multivariate normal distributions; and we improve the numerical implementation via Monte Carlo simulations for the inference from partial information of generalized expectation type.
