Implementation of common inference algorithms for Bayesian networks. Allows for efficient exact and approximate inference that works both in low- and high-dimensional settings. Efficient marginalization is reached by splitting the calculation into sub-calculations of lower dimensionality. Implemented approximate inference algoti Gibbs sampling, loopy belief propagation and SubGroupSeparation. Implemented exact inference methods: SubGroupSeparation (fastest), junction-tree algorithm, complete enumeration. Implemented approximate inference methods: SubGroupSeparation (highest accuracy), loopy belief propagation, Markov chain Monte Carlo (MCMC) sampling. References: Bayer, F.M., Moffa, G., Beerenwinkel, N. and Kuipers, J., 2021. High-Dimensional Inference in Bayesian Networks. arXiv preprint <doi:10.48550/arXiv.2112.09217>.