Month: August 2016

In the last post I have introduced the probabilistic programming. The biggest problem this idea is to find an efficient approximation of the posterior for arbitrary probabilistic models. Auto-Encoding Variational Bayes (AEVB) is a great step into the right direction. Consider a dataset . It consists of i.i.d. continues samples of dimension D. The data

I love the idea of Probabilistic Programming. It is basically a language to describe probabilistic models and then perform automatic inference on those models. That means that you as an expert write a simulation of your problem in a Bayesian sense. For example, a text simulation could be to first sample the number of words,