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Following is a Sigmoid Belief Networks where we can only observe the bottom observable layer $v.$ Usually we use Wake-Sleep to trainning the bottom-to-up Recognition Weight $r_{ji}$ and up-to-bottom Generative Weight $w_{ij}.$ The conditional probability of all the nodes except for the top layer root nodes $(h^{(2)}_1,h^{(2)}_2)$ are assumed to be the Bernoulli distribution with Logistic probability function. There is no assumption of the probability function on $(h^{(2)}_1,h^{(2)}_2).$

As a generative model, how do we generate the samples of roots $(h^{(2)}_1,h^{(2)}_2)?$ enter image description here

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From this tutorial:

To do this, for each unit of the top layer, the sigmoid function is simply applied to the additive constant in (1), as there are no subsequent layers providing influence.

This means only a bias is associated with the top random variables to parametrize the logistic function:

$p(h^{(2)}_1=1)=\frac{1}{1 + \exp(-b^{(2)}_1)}$ and $p(h^{(2)}_2=1)=\frac{1}{1 + \exp(-b^{(2)}_2)}$.

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