Probabilistic AND/EXOR networks have been defined, in the past, as a class of Reed-Muller circuits, which operate on random signals. In contemporary logic network design, it is classified as behavioral notation of probabilistic logic gates and networks. In this paper, we introduce additional notations of probabilistic AND/EXOR networks: belief propagation, stochastic, decision diagram, neuromorphic models, and Markov random field model. Probabilistic logic networks, and, in particular, probabilistic AND/EXOR networks, known as turbo-decoders (used in cell phones and iPhone) are in demand in the coding theory. Another example is intelligent decision support in banking and security applications. We argue that there are two types of probabilistic networks: traditional logic networks assuming random signals, and belief propagation networks. We propose the taxonomy for this design, and provide the results of experimental study. In addition, we show that in forthcoming technologies, in particular, molecular electronics, probabilistic computing is the platform for developing the devices and systems for low-power low-precise data processing.