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: norman@agenarisk.com

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