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bayesian inference中文是什么意思

发音:  
用"bayesian inference"造句"bayesian inference"怎么读"bayesian inference" in a sentence

中文翻译手机手机版

  • 贝叶斯定理
  • 贝叶斯推断
  • 贝叶斯推理
  • 贝叶斯推论

例句与用法

  • Decision tree model based on bayesian inference
    基于贝叶斯推理的决策树模型
  • Target tracking based on bayesian inference
    基于贝叶斯推理的目标跟踪
  • Bayesian inference network
    贝叶斯推理网络
  • Bayesian inference for two - parameter exponential distribution under type - doubly censored in a nonlinear wave equation
    双边定数截尾场合双参数指数分布的贝叶斯推断
  • Furthermore , the bayesian inference theory about unrestricted and restricted var ( p ) model under the parameter ' s prior distributions is explored . the structure of minnesota conjugate prior distribution , its hyper - parameters and determination , and the bayesian theory about var ( p ) model under the special conjugate prior distribution are all analyzed in detail
    其次,探讨了非限制性和限制性var ( p )预测模型的贝叶斯推断理论,系统地分析了著名的minnesota共轭先验分布的结构及其超参数的设置,以及该先验分布下var ( p )模型的贝叶斯推断。
  • This paper mainly deals with the multivariate bayesian inference theory used in the modern economical and management science . this includes the bayesian inference theory about three important kinds of linear models , including the single equation model , multiple equation model system and var ( p ) predictive model , and their application in economic forecasting and quality control , and also the design for the bayesian classification identification method among multiple populations
    本文主要研究现代经济管理中的多元贝叶斯推断理论,包括单方程模型、多方程模型系统和向量自回归var ( p )模型的贝叶斯推断理论及其在经济预测与质量控制中的应用,以及多总体的贝叶斯分类识别方法的构造。
  • On the strength of the square loss function , this part also defines the vector loss function and matrix loss function , and discusses the bayesian risk decision solutions about random vector parameter and random matrix parameter under these loss functions respectively . secondly , the bayesian inference theory about single equation model is explored
    在单参数平方损失函数的基础上,定义了向量损失函数,利用向量化算子vec定义了矩阵损失函数,并讨论了这两类损失函数下随机向量参数和随机矩阵参数的贝叶斯风险决策解。
用"bayesian inference"造句  
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