ProbCog is a statistical relational learning and reasoning system that supports efficient learning and inference in relational domains. We provide an extensive set of open-source tools for both undirected and directed statistical relational models.
The following representation formalisms for probabilistic knowledge are supported:
- Bayesian Logic 网络s (BLNs)
- Markov Logic 网络s (MLNs) & Adaptive Markov Logic 网络s (AMLNs)
- Bayesian 网络s (BNs)
Though ProbCog is a general-purpose software suite, it was designed with the particular needs of technical systems in mind. Our methods are geared towards practical applicability and can easily be integrated into other applications. The tools for relational data collection and transformation facilitate data-driven knowledge engineering, and the availability of graphical tools makes both learning or inference sessions a user-friendly experience. Scripting support enables automation, and for easy integration into other applications, we provide a client-server library.
The ProbCog维基 provides all the information you will need to get started. In particular, the following pages might be relevant at this stage:
- Dominik Jain
- Stefan Waldherr
- Klaus von Gleissenthall
- Andreas Barthels
- Ralf Wernicke
- Gregor Wylezich
- Martin Schuster
- Philipp Meyer
- Daniel Nyga