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  1. MATH-probability

Variational Inference

相比MCMC,在大数据量采样下,VI要快。 1

2

3 真实的后验概率P(Z∣X)P(Z|X)P(Z∣X)往往是十分复杂的,我们用q(Z)q(Z)q(Z)近似P(Z)P(Z)P(Z) , 并且选择q(Z)=q1(Z1)q2(Z2)⋯qM(ZM)=∏i=1Mqi(Zi)q(Z)=q_1(Z_1)q_2(Z_2)⋯q_M(Z_M) = \prod_{i=1}^{M}q_{i}(Z_{i})q(Z)=q1​(Z1​)q2​(Z2​)⋯qM​(ZM​)=∏i=1M​qi​(Zi​),将每个q分解。这样便于计算积分等。这叫做平均场理论(mean field theory),主要基于基于系统中个体的局部相互作用可以产生宏观层面较为稳定的行为这个物理思想。

l1部分,只关心j部分

l2部分,只关心第j部分

4 所以最终L(q)L(q)L(q)可以简化成:

再简化:

参考佳文

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Variational Inference with Implicit Probabilistic Models: Part 1:Bayesian Logistic Regression
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