Jajapy: A Learning Library for Stochastic Models

We present Jajapy, a Python library that implements a number of methods to aid the modelling process of Markov models from a set of partially-observable executions of the system. Currently, Jajapy supports different types of Markov models such as discrete and continuous-time Markov chains, as well as Markov decision processes. Jajapy can be used both to learn the model from scratch or to estimate parameter values of a given model so that it fits the observed data the best. To this end, the tool offers different learning techniques, either based on expectation-maximization or state-merging methods, each adapted to different types of Markov models. One key feature of Jajapy consists in its compatibility with the model checkers Storm and Prism. The paper briefly presents Jajapy’s functionalities and reports an empirical evaluation of their performance and accuracy. We conclude with an experimental comparison of Jajapy against AALpy, which is the current state-of-the-art Python library for learning automata. Jajapy and AALpy complement each other, and the choice of the library should be determined by the specific context in which it will be used.