Introduction
Licensing
What's new in SMILE 2
Platforms and Wrappers
Java and jSMILE
Maven
Python and PySMILE
R and rSMILE
.NET and SMILE.NET
Hello, SMILE Wrapper!
Success/Forecast model
VentureBN.xdsl
The program
Hello.java
Hello.py
Hello.R
Hello.cs
Using SMILE Wrappers
Error handling
Networks, nodes and arcs
Network
Nodes
Arcs
Anatomy of a node
Node definition
Node value
Node evidence
Other node attributes
Multidimensional arrays
Input and Output
Inference
User properties
Submodels
Discrete nodes and numeric domains
Outcome intervals
Outcome point values
Canonical nodes
Noisy-MAX
Noisy-Adder
Influence diagrams
Dynamic Bayesian networks
Unrolling
Temporal definitions
Temporal evidence
Temporal beliefs
Continuous models
Equation-based nodes
Continuous inference
Hybrid models
Equations reference
Operators
Random Number Generators
Statistical Functions
Arithmetic Functions
Combinatoric Functions
Trigonometric Functions
Hyperbolic Functions
Logical/Conditional functions
Custom Functions
Diagnosis
Diagnostic roles
Observation cost
Diagnostic session
Distance and entropy-based measures
Learning
Learning network structure
Learning network parameters
Validation
Tutorials
Tutorial 1: Creating a Bayesian Network
Tutorial1.java
Tutorial1.py
Tutorial1.R
Tutorial1.cs
Tutorial 2: Inference with a Bayesian Network
Tutorial2.java
Tutorial2.py
Tutorial2.R
Tutorial2.cs
Tutorial 3: Exploring the contents of a model
Tutorial3.java
Tutorial3.py
Tutorial3.R
Tutorial3.cs
Tutorial 4: Creating the Influence Diagram
Tutorial4.java
Tutorial4.py
Tutorial4.R
Tutorial4.cs
Tutorial 5: Inference in an Influence Diagram
Tutorial5.java
Tutorial5.py
Tutorial5.R
Tutorial5.cs
Tutorial 6: Dynamic model
Tutorial6.java
Tutorial6.py
Tutorial6.R
Tutorial6.cs
Tutorial 7: Continuous model
Tutorial7.java
Tutorial7.py
Tutorial7.R
Tutorial7.cs
Tutorial 8: Hybrid model
Tutorial8.java
Tutorial8.py
Tutorial8.R
Tutorial8.cs
Tutorial 9: Structure learning
Tutorial9.java
Tutorial9.py
Tutorial9.R
Tutorial9.cs
Acknowledgments