In my thesis "A Coding Perspective on Deep Latent Variable Models", we discuss how statistical inference in Deep Latent Variable Models (DLVMs) relates to coding. In particular, we examine the minimum deception length (MDL) principle as a guide for statistical inference. In this context, we explore its relation to Bayesian inference. We shall see that despite both leading to similar algorithms, the MDL principle allows us to make no assumption about the data generating process. We merely restrict ourselves to find…