The chain structure is also convenient to handle streaming data as we will explain later. We first give a brief introduction to the EP and CEP framework. Step 2. We construct a tilted distribution to combine the true likelihood, Step 3. We project the tilted distribution back to the exponential family, q KL( null p nullq) where q belongs to the exponential family. Step 4. We update the approximation term by's in parallel, and uses damping to avoid divergence. The above computation are very conveniently to implement.

In this section, we provide proof for the disentanglement identifiability of the inferred exogenous variable. Our proof consists of three main components. Then we have ( f, T, λ) ( f, T, λ) . The conditional V AE, in this case, inherits all the properties of maximum likelihood estimation. The following proof is based on the reduction to absurdity.

How biological neural networks learn in a supervised manner has long been an open problem.