Uniform Random Sampling of Traces in Very Large Models

TL;DR

This paper introduces a method for performing uniform random sampling of traces in very large models composed of communicating modules, using techniques for counting and sampling in regular languages without constructing the entire global model.

Contribution

It presents a novel approach to achieve uniform random walks in large models by combining local uniform sampling of modules, avoiding the need for global model construction.

Findings

Effective uniform sampling in large models without global model construction

Combines local automaton-based sampling techniques

Applicable to models described as communicating reactive modules

Abstract

This paper presents some first results on how to perform uniform random walks (where every trace has the same probability to occur) in very large models. The models considered here are described in a succinct way as a set of communicating reactive modules. The method relies upon techniques for counting and drawing uniformly at random words in regular languages. Each module is considered as an automaton defining such a language. It is shown how it is possible to combine local uniform drawings of traces, and to obtain some global uniform random sampling, without construction of the global model.