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New Developments in Bayesian Network Software ( AgenaRisk ) Fifth Annual Conference of the Australasian Bayesian Network Modelling Society (ABNMS2013), Hobart, Tasmania , 28 Nov 2013. Norman Fenton Web: www.AgenaRisk.com Email: [email protected]. Key differentiating features. - PowerPoint PPT Presentation
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New Developments in Bayesian Network Software (AgenaRisk)
Fifth Annual Conference of the Australasian Bayesian Network Modelling Society (ABNMS2013), Hobart, Tasmania,
28 Nov 2013
Norman FentonWeb: www.AgenaRisk.com
Email: [email protected]
Key differentiating featuresRisk Table view (tailorable questionnaire)Multiple scenariosSimulation and dynamic discretization (leading to intelligent parameter and table learning)Sensitivity analysis and multivariate analysisBinary factorizationParameter Passing between modelsRanked nodesComprehensive models and tutorialsA free version with full standard BN functionality
Risk explorer view (linked
BNOs
Simulation node tool
Sensitivity analyser
Multivariate analyser
Simulation node
Ranked node
Expanding a node monitor
Statistics
State values
Changing graph
defaults
Defining the states of a numeric (simulation node)
That’s it. No need to worry about discretization intervals
Static v Dynamic Discretization
Static v Dynamic Discretization
Result has mean 25
Result has mean 30
Multiple scenarios
Multiple scenarios in Risk Table view
Sensitivity Analyser
Sensitivity Analyser
Sensitivity Analyser Results
Statistical distributions
Parameter learning: priors
Parameter learning: 2 data points
Parameter learning: 7 data points
Parameter learning: inconsistent data
Binary factorization
Parameter Passing
Parameter Passing
Solves classic BN problem of how to access just the summary statistics for a node
Ranked nodes example
Whole NPT defined in seconds
Whole NPT defined in seconds
Priors
Impact of some observations
Add testing effort
Now backwards inference
Only want to spend minimal effort
..and staff have average experience
Change the scale
Instant rescaling
AgenaRisk VersionsAgenaRisk
FreeAgenaRisk
Lite AgenaRisk
ProOpen and run any model Yes Yes YesRisk map, risk table, and risk explorer views Yes Yes YesFully configurable risk graphs Yes Yes YesSensitivity analysis Yes Yes YesMultivariate analysis Yes Yes YesImport/export functionality Yes Yes YesCreate new model Yes Yes YesPre-supplied models, tutorials, User manual Yes Yes YesSave Model containing just Boolean and labelled nodes
Yes Yes Yes
Save model containing ranked nodes max 5 max 10 Unlimited
Save model containing simulation nodes max 5 max 10 UnlimitedSave model containing multiple BNOs max 2 max 5 Unlimited
Maintenance support None None UnlimitedUpgrades None None UnlimitedCost Free Free to buyers of
bookSubscription
Also API Version available
Supporting Book
CRC Press, ISBN: 9781439809105 , ISBN 10: 1439809100
www.bayesianrisk.com
1. There is more to assessing risk than statistics 2. The need for causal explanatory models in risk assessment3. Measuring uncertainty: the inevitability of subjectivity4. The Basics of Probability 5. Bayes Theorem and Conditional Probability6. From Bayes Theorem to Bayesian Networks
7. Defining the Structure of Bayesian Networks8. Building and Eliciting Probability Tables9. Numeric Variables and Continuous Distribution Functions10. Hypothesis Testing and Confidence Intervals 11. Modeling Operational Risk12. Systems Reliability Modeling13. Bayes and the Law
Supporting Book Chapters
Plus extensive resources and models at www.bayesianrisk.com
Future Releases
Version 6.1 (Dec 2013)New algorithm with enhanced DD accuracy and efficiencyMany additional models
Web services versionBAYES-KNOWLEDGE add-ons
www.eecs.qmul.ac.uk/~norman/projects/B_Knowledge.html