A Cancellation Law for Probabilistic Processes

09/13/2023
by   Rob van Glabbeek, et al.
0

We show a cancellation property for probabilistic choice. If distributions mu + rho and nu + rho are branching probabilistic bisimilar, then distributions mu and nu are also branching probabilistic bisimilar. We do this in the setting of a basic process language involving non-deterministic and probabilistic choice and define branching probabilistic bisimilarity on distributions. Despite the fact that the cancellation property is very elegant and concise, we failed to provide a short and natural combinatorial proof. Instead we provide a proof using metric topology. Our major lemma is that every distribution can be unfolded into an equivalent stable distribution, where the topological arguments are required to deal with uncountable branching.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/28/2018

A combinatorial proof of the extension property for partial isometries

We present a short and self-contained proof of the extension property fo...
research
12/16/2019

Synthetic topology in Homotopy Type Theory for probabilistic programming

The ALEA Coq library formalizes measure theory based on a variant of the...
research
09/22/2019

Probabilistic Fitting of Topological Structure to Data

We define a class of probability distributions that we call simplicial m...
research
09/01/2022

Probabilistic Deduction: an Approach to Probabilistic Structured Argumentation

This paper introduces Probabilistic Deduction (PD) as an approach to pro...
research
12/04/2017

Layer by layer - Combining Monads

We develop a method to incrementally construct programming languages. Ou...
research
12/09/2021

Testing Probabilistic Circuits

Probabilistic circuits (PCs) are a powerful modeling framework for repre...
research
05/27/2022

Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design

Our world is ambiguous and this is reflected in the data we use to train...

Please sign up or login with your details

Forgot password? Click here to reset