3 papers
cs.LO2025
Compositional Inference for Bayesian Networks and Causality
Bart Jacobs, Márk Széles, Dario Stein
Inference is a fundamental reasoning technique in probability theory. When applied to a large joint distribution, it involves updating with evidence (conditioning) in one or more c…
math.CT2025
Normalized Probabilistic Semantics is Not Associative
Elena Di Lavore, Mario Román, Márk Széles
Normalization, , fails to form a distributive law, forcing the composition of normalized stochastic kernels to be non-associative. We introduce a first norma…
cs.LO2025
Order in Partial Markov Categories
Elena Di Lavore, Mario Román, Paweł Sobociński +1
Partial Markov categories are a recent framework for categorical probability theory that provide an abstract account of partial probabilistic computation with updating semantics. I…