Starting with version 2.0, SMILE fully supports Macs based on M series (Apple Silicon) ARM CPUs. The C++, Python and Java libraries are available as universal binaries, containing both ARM and x64 code. The wrapper for R is available as separate binaries for ARM and x64. To download the libaries, go to https://download.bayesfusion.com.
SMILE 2.0 is now available. This version of the library supports discrete node outcomes based on numeric intervals or point values. Also, the metalog probability distribution can be used in equation node definitions. The libraries for C++, Python, Java, R and .NET can be downloaded from https://download.bayesfusion.com. We also maintain repositories for use with Maven and […]
GeNIe 4.0 is now available at https://download.bayesfusion.com. Most important new features are: discrete nodes with outcomes based on numeric intervals or point values metalog distribution, including interactive metalog builder tool geospatial processing added, Esri ASCII raster grids supported new Distribution Visualizer window
How to use AWS Lambda with SMILE and more – see our new video:
BayesFusion’s Maven repository for jSMILE is now available. If you use jSMILE in a Maven-based project, you can reference the library directly in your POM file. For more details (including native library integration in POM), please refer to the Platforms and Wrappers/Java and jSMILE /Maven section in SMILE Wrappers Programmer’s Manual at our documentation website: https://support.bayesfusion.com/docs/
SMILE library running on an ESP32 microcontroller
This is a 14-hour course covering the principles of probabilistic modeling using Bayesian networks, building Bayesian networks based on expert knowledge (both structure and numerical parameters), dynamic Bayesian networks, learning Bayesian networks from data and causal discovery, parameter learning, validation techniques, elements of expected utility theory, utility elicitation, and influence diagrams. Meeting times: The […]
This is a 12-hour course covering the principles of probabilistic modeling using Bayesian networks, building Bayesian networks based on expert knowledge (both structure and numerical parameters), dynamic Bayesian networks, learning Bayesian networks from data and causal discovery, parameter learning, validation techniques, elements of expected utility theory, utility elicitation, and influence diagrams. Meeting times: The […]
The Program Committee of the 10th Probabilistic Graphical Models (PGM 2020) conference announced the winner of the BayesFusion Best Student Paper Award in Aalborg, Denmark, on September 25. The winner is: Alessandro Bregoli, Universita degli Studi di Milano-Bicocca, Milano, Italy, for the paper entitled Constraint-Based Learning for Continuous-Time Bayesian Networks, co-authored with Marco Scutari and […]
BayesFusion releases SMILE 1.6. This version fully supports Unicode in node identifiers, names, and other textual attributes stored in models. To download the library, visit https://download.bayesfusion.com The documentation is available at https://support.bayesfusion.com/docs
