matlab求导代码-VariationalGaussianCopula:Matlab变分高斯Copula推论
matlab求导代码变分高斯Copula推论
我们使用高斯copulas（与固定/自由形式的边距组合）作为自动推理引擎，以对通用分层贝叶斯模型进行变分近似（仅有的两个特定于模型的项是对数似然和先验项及其派生词）。
我们评估了在单变量页边距中复制的特殊性以及在潜在变量之间广泛捕获的后验依赖。
本文的Matlab代码
韩少波，廖学军，David
B.Dunson和Lawrence
Carin，第19届人工智能与统计国际会议（AISTATS
2016）
，西班牙加的斯，2016年5月
例子
演示1：边际适应（偏斜，学生的t，Beta和Gamma）
>>
demo_SkewNormal
>>
demo_StudentT
>>
demo_Gamma
>>
demo_Beta
实数，正实数和截断的[0,1]变量的边际逼近的精度如下所示，
演示2：双变量对数正态
>>
demo_BivariateLN
我们使用具有（1）固定形式对数正态分布裕度（2）基于自由形式伯恩斯坦多项式的裕度的双变量高斯copula近似双变量对数正态分布，
演示3：马蹄收缩
基准比较包括：
吉布斯采样器
平均场VB
VGC-L
文件下载
资源详情
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