Modeling / SDK

ProBT

ProBT© is a powerful modeling, inference, and learning platform inheriting 10 years of experience in the domain of Bayesian probabilistic computing.

ProBT extends the Bayesian Networks framework by providing a structured programming language allowing the developers to increase their applications capabilities and robustness by easily integrating Bayesian models.

To fit any professional usage, Probayes optimized ProBT delivery through a set of modules:

 ProBT-Engine ensures ProBT full power to programmers. Available through a library API available in C++ and Python.

 ProBT-XL makes Bayesian models prototyping and testing easier and quicker. It provides a Microsoft Excel graphical user interface to ProBT. Use it to design Bayesian networks while taking advantage of the worksheet environment and the Bayesian programming approach. Models designed by ProBT-XL can be exported to be executed autonomously by ProBT-Engine.

Bayesian Occupancy Filter (BOF)

The Bayesian Occupancy Filter (BOF) technology provides a strong theoretical framework for robust sensing and multi-target tracking in dynamic environments using imperfect data coming from various sensors.

BOF is useful in a large category of applications such as:

 Driving assistance and safety,

 Autonomous robots,

 Sensor-based surveillance,

 Smart interactive toys.