"Probabilistic programming is an emergent field based on the idea that probabilistic models can be efficiently represented as executable code. This idea has enabled researchers to formalize, automate, and scale up many aspects of modeling and inference; to make modeling and inference accessible to a broader audience of developers and domain experts; and to develop new programmable AI systems that integrate modeling and inference approaches from multiple domains." (Program comitee of The International Conference on Probabilistic Programming - PROBPROG 2020 -, 2020/08/03 )

In order to model complex phenomena like vision, communication and planning, we need a representation of uncertainty that is powerful enough to capture very general stochastic processes. Modelers specify a probabilistic model in its entirety (e.g., by writing code in a PPL that generates a sample from the joint distribution) and inference follows automatically given the specification.

Here we present a zoo of mostly generative probabilistic models written in classical or upcoming probabilistic programming languages (PPLs). This model zoo is aimed to be an inspiration for our students coming from various disciplines like computational science, psychology, and physics.

We present generative Bayesian models embedded in Turing, Greta, PyRo, WebPPL, WebCHURCH, OpenBUGS, Figaro, and PROBT for cognitive and reactive aspects of agents' behavior and the development of assistance systems. Generative models are typically causal models. They abstractly represent causal processes in the real world and possess the ability to generate data or evaluate their likelihood.