Joint Probability Trees (short JPTs) are a formalism for learning of and reasoning about joint probability distributions, which is tractable for practical applications. JPTs support both symbolic and subsymbolic variables in a single hybrid model, and they do not rely on prior knowledge about variable dependencies or families of distributions.