PROFET: Construction and Inference of DBNs Based on Mathematical Models

TL;DR

PROFET is an open-source software tool that automatically constructs Dynamic Bayesian Networks from differential equations, enabling robust temporal inference and parameter estimation in uncertain and noisy data environments.

Contribution

It introduces an automated method to build DBNs from ODE models and extends particle filtering with adaptive time steps for improved temporal inference.

Findings

Successfully inferred model variables in benchmark ODE systems.

Automated process from model generation to inference.

Open-source, platform-independent implementation.

Abstract

This paper presents, evaluates, and discusses a new software tool to automatically build Dynamic Bayesian Networks (DBNs) from ordinary differential equations (ODEs) entered by the user. The DBNs generated from ODE models can handle both data uncertainty and model uncertainty in a principled manner. The application, named PROFET, can be used for temporal data mining with noisy or missing variables. It enables automatic re-estimation of model parameters using temporal evidence in the form of data streams. For temporal inference, PROFET includes both standard fixed time step particle filtering and its extension, adaptive-time particle filtering algorithms. Adaptive-time particle filtering enables the DBN to automatically adapt its time step length to match the dynamics of the model. We demonstrate PROFET's functionality by using it to infer the model variables by estimating the model parameters of four benchmark ODE systems. From the generation of the DBN model to temporal inference, the entire process is automated and is delivered as an open-source platform-independent software application with a comprehensive user interface. PROFET is released under the Apache License 2.0. Its source code, executable and documentation are available at http:://profet.it.nuigalway.ie.