  Despite the recent successes of probabilistic programming languages (PPLs) in
AI applications, PPLs offer only limited support for random variables whose
distributions combine discrete and continuous elements. We develop the notion
of measure-theoretic Bayesian networks (MTBNs) and use it to provide more
general semantics for PPLs with arbitrarily many random variables defined over
arbitrary measure spaces. We develop two new general sampling algorithms that
are provably correct under the MTBN framework: the lexicographic likelihood
weighting (LLW) for general MTBNs and the lexicographic particle filter (LPF),
a specialized algorithm for state-space models. We further integrate MTBNs into
a widely used PPL system, BLOG, and verify the effectiveness of the new
inference algorithms through representative examples.
