Unifying Inference for Bayesian and Petri Nets

07/17/2018
by   Roberto Bruni, et al.
0

Recent work by the authors equips Petri occurrence nets (PN) with probability distributions which fully replace nondeterminism. To avoid the so-called confusion problem, the construction imposes additional causal dependencies which restrict choices within certain subnets called structural branching cells (s-cells). Bayesian nets (BN) are usually structured as partial orders where nodes define conditional probability distributions. In the paper, we unify the two structures in terms of Symmetric Monoidal Categories (SMC), so that we can apply to PN ordinary analysis techniques developed for BN. Interestingly, it turns out that PN which cannot be SMC-decomposed are exactly s-cells. This result confirms the importance for Petri nets of both SMC and s-cells.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/12/2017

Concurrency and Probability: Removing Confusion, Compositionally

Assigning a satisfactory truly concurrent semantics to Petri nets with c...
research
02/27/2023

Relating Reversible Petri Nets and Reversible Event Structures, categorically

Petri nets, where causal dependencies are modelled via inhibitor arcs, c...
research
09/30/2020

Uncertainty Reasoning for Probabilistic Petri Nets via Bayesian Networks

This paper exploits extended Bayesian networks for uncertainty reasoning...
research
06/29/2018

Updating Probabilistic Knowledge on Condition/Event Nets using Bayesian Networks

The paper extends Bayesian networks (BNs) by a mechanism for dynamic cha...
research
03/01/2021

On Causal Semantics of Petri Nets

We consider approaches for causal semantics of Petri nets, explicitly re...
research
04/29/2019

Computational Petri Nets: Adjunctions Considered Harmful

We review some of the endeavors in trying to connect Petri nets with fre...
research
08/06/2012

Credal nets under epistemic irrelevance

We present a new approach to credal nets, which are graphical models tha...

Please sign up or login with your details

Forgot password? Click here to reset