• 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: Diagnosis
    • Tutorial9.java
    • Tutorial9.py
    • Tutorial9.R
    • Tutorial9.cs
  • Tutorial 10: Structure learning
    • Tutorial10.java
    • Tutorial10.py
    • Tutorial10.R
    • Tutorial10.cs
• Acknowledgments