I am dealing with two scenarios: 1) Non-Gaussian data distribution and 2) non-stationary data).
First, I am planning to use a variational autoencoder for modeling the probability distribution of the non-Gaussian data distribution in the latent space. (Note, the input of the encoder part will be the non-Gaussian data). Then, I will it to perform some classification tasks.
However, is it possible to use the variational autoencoder to deal with non-Gaussian Distribution data? (because the non-Gaussian data is not generated from a Gaussian distribution).
Second, I also want to use the variational autoencoder to deal with non-stationary data. Would it be possible to use it with non-stationary data or should I consider other ML techniques.