1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
Order in Partial Markov Categories
Elena Di Lavore, Mario Román, PaweÅ SobociÅski +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…
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…
Combs, Causality and Contractions in Atomic Markov Categories
Dario Stein, Márk Széles
We present a counterexample showing that Markov categories with conditionals (such as BorelStoch) need not validate a natural scheme of axioms which we call contraction identities.…