SMILE: Structural Modeling, Inference, and Learning Engine
SMILE is a reasoning and learning/causal discovery engine for graphical models, such as Bayesian networks, influence diagrams, and structural equation models. Technically, it is a library of C++ classes that can be embedded into existing user software through its API, enhancing user products with decision modeling capabilities. SMILE is fully portable and available for most computing platforms – from data center to embedded. We offer wrappers for SMILE that make it possible to use it from Java, Python, R, .NET, and other development environments.
Functionality of SMILE
- Complete coverage of the field of probabilistic graphical models (Bayesian networks, dynamic Bayesian networks, influence diagrams)
- Variety of state of the art exact and approximate reasoning algorithms, relevance-based inference.
- Discrete and continuous variables and probability distributions, equation-based interactions.
- Canonical interaction models, such as Noisy-OR/AND/MAX/MIN.
- Most complete implementation of influence diagrams in the field (calculation of expected utilities for all decision strategies, multiple utility nodes, multi-attribute utility functions).
- Structure and parameter learning, incremental learning and model refinement, causal discovery.
- Model validation, including ROC and calibration curves.
- Support for diagnostic applications, such as optimal selection of questions and tests.
Why choose SMILE
- Performance leader in the field of probabilistic graphical models.
- Platform independent, versions available for Windows, Linux, Mac, Android, etc. We can compile SMILE for your platform on request.
- Used in web, desktop, and mobile applications.
- jSMILE available for use with Java. jSMILE can be also used from other environments that can use JVM.
- PySMILE available for use with Python.
- rSMILE for R
- SMILE.NET available for use with .NET framework.
- SMILE.COM for easy integration with MS Excel.
- Thorough documentation and tutorials.
- Robust and running successfully in the field since 1997