We offer a 2 day training course in Bayesian networks, using Bayes Server™. We offer both private and public training events.

Private events


If you would like us to conduct training within your organisation, please feel free to contact us with your requirements.


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Public events

Upcoming public events are listed below.


Please confirm places are still available before purchasing. You can purchase online, or send us a purchase order.


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Course outline

A course outline is provided below. This is subject to change.

Day 1


  • What is a variable?
    • Variables - discrete & continuous
    • Discretization?
  • Probability foundations
    • Probability
    • Joint probability
    • Conditional probability
    • Marginal probability
    • Multiplication
    • Instantiation
    • Division
    • Bayes Theorem
    • Conditional independence
    • Exercise - probability
  • Bayesian networks - construction
    • Bayesian networks - Definition
    • Adding nodes & variables
    • Nodes from data
    • Adding links
    • Parameter counter
    • Links from data
    • Distributions & parameters
    • The node toolbar
    • Exercise - construction
  • Bayesian networks - inference part I
    • Adding queries
    • Query explorer
    • Evidence
    • Prediction & diagnostics
    • Converging, diverging and serial connections
    • Exercise - inference
  • Inference with data
    • What is a case?
    • Using databases and spreadsheets
    • Data explorer
    • Batch queries
    • Mesh queries
    • Exercise - data connection
    • Exercise - inference with data
  • Bayesian networks - parameter learning part I
    • Learning parameters
    • Candidate networks
    • Log likelihood
    • Exercise - parameter learning
  • Bayesian networks - structural learning
    • Structural learning algorithms
    • Exercise - structural learning
  • Analysis - part I
    • Value of information
    • Pattern Analysis
    • Automated insight
    • Link Strength
    • D-separation
  • Decision graphs
    • Understanding Decision Graphs
  • Latent variables
    • Latent variables
  • Segmentation/clustering
    • What is clustering?
    • Mixture models
    • Exercise - clustering
  • Regression (Optional)
    • What is regression?
    • A simple model - linear regression
    • Exercise - regression
  • Classification (Optional)
    • What is classification?
    • A simple model - Naive Bayes
    • Confusion matrix
    • Lift chart
    • Exercise - classification


Day 2


  • Noisy nodes
    • Noisy nodes
  • Mult-variate nodes
    • Multi-variable nodes
    • Decomposition
    • Exercise - multi variable nodes
  • Bayesian networks - inference part II
    • Joint queries
    • Log likelihood
    • Conflict
    • Retracted evidence
    • Most Probable Explanation
    • Comparison queries
    • Exercise - inference part II
    • Basic algorithm - Variable elimination
    • Inference algorithms
  • Analysis - part II
    • Impact Analysis
    • Log-likelihood Analysis
    • Exercise - Log-likelihood Analysis
    • Tree width
    • Sensitivity Analysis
    • Parameter tuning
    • In-Sample Anomalies
    • Retracted analysis
  • Modelling techniques
    • Divorcing
    • Expert disagreements
    • Exercise - modelling techniques (Optional)
  • Dynamic Bayesian networks
    • Temporal variables
    • Temporal links
    • Temporal distributions
    • Unrolling
    • Exercise - unrolling
  • Dynamic Bayesian networks - inference
    • Queries (time series)
    • Query explorer (time series queries)
    • Temporal evidence
    • Data explorer (time series data)
    • Exercise - time series inference
  • Bayesian networks - parameter learning part II
    • Filtering
    • Initialization
    • Time series distributions
    • Learning with missing data
    • Case weights
    • Exercise - parameter learning part II
  • Bayesian networks - online learning
    • What is online learning (adaptation)?
  • Bayesian networks - data sampling
    • Generating sample data
    • Charting sampled data
    • Missing data
    • Writing to a data source
    • Exercise - data sampling
  • Anomaly detection
    • What is anomaly detection?
    • Exercise - anomaly detection
  • Bayesian networks - using the Bayes Server library (API)
    • Exercise - using the Bayes Server library
  • Time series models (Optional)
    • Types of time series model
    • Prediction, filtering, smoothing and most probable sequences
    • Exercise - time series models
  • Arc Reversal (Optional)
    • Arc reversal
    • Exercise - Arc Reversal


User interface

  • Windows 10 (x86 and x64)
  • Windows Server 2016 (x86 and x64)
  • Windows 8 (x86 and x64)
  • Windows Server 2012 (x64)
  • Windows 7 (x86 and x64)
  • Windows Vista (x86 & x64) with Service Pack 2
  • Windows XP (x86) with Service Pack 3
  • Windows Server 2008 (x86 and x64) with Service Pack 2
  • Windows Server 2008 R2 (x64)
  • Windows Server 2003 (x86 & x64) with Service Pack 2
  • Windows Server 2003 R2 (x86 and x64)