continuous non-parametric bayesian networks in uninet · • kurowicka, d. , cooke, r.m. (2011)...
TRANSCRIPT
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Continuous non-parametric Bayesian networks
in Uninet
dan ababei
light twist software
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A Bayesian network represents a joint distribution • Discrete joint distributions • Continuous joint distributions
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A Bayesian network represents a joint distribution • Discrete joint distributions • Continuous joint distributions
A Bayesian network consists of
• Qualitative part • Quantitative part
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A Bayesian network’s qualitative part is the DAG
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A Bayesian network’s quantitative part is how the nodes and arcs are quantified
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socioecon
index
<60 60-75 75-90 90-100
0.0
00
0.0
05
0.0
10
0.0
15
0.0
20
age
years
<20 20-30 30-45 45-60 >60
0.0
00
0.0
05
0.0
10
0.0
15
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25
0.0
30
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Discrete Bayesian network
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socioecon
index
<60 60-75 75-90 90-100
0.0
00
0.0
05
0.0
10
0.0
15
0.0
20
age
years
<20 20-30 30-45 45-60 >60
0.0
00
0.0
05
0.0
10
0.0
15
0.0
20
0.0
25
0.0
30
0.0
35
CPT
socioecon | age
sociecon
age low to middle upper middle high top
very young 0.79 0.19 0.02 0
young 0.32 0.48 0.18 P(socioecon=top|age=young) = 0.02
mature 0.03 0.28 0.6 0.09
middle-age 0 0.03 0.48 0.49
elderly 0.01 0.01 0.08 0.9
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socioecon
index
<60 60-75 75-90 90-100
0.0
00
0.0
05
0.0
10
0.0
15
0.0
20
age
years
<20 20-30 30-45 45-60 >60
0.0
00
0.0
05
0.0
10
0.0
15
0.0
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age
years
20 40 60 80
05
10
15
20
25
30
socioecon
index
20 40 60 80 100
05
10
15
20
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age
years
20 40 60 80
05
10
15
20
25
30
socioecon
index
20 40 60 80 100
05
10
15
20
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age
years
20 40 60 80
05
10
15
20
25
30
socioecon
index
20 40 60 80 100
05
10
15
20
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age
years
20 40 60 80
05
10
15
20
25
30
socioecon
index
20 40 60 80 100
05
10
15
20
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rank correlation
age
years
20 40 60 80
05
10
15
20
25
30
socioecon
index
20 40 60 80 100
05
10
15
20
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rank correlation
age
years
20 40 60 80
05
10
15
20
25
30
socioecon
index
20 40 60 80 100
05
10
15
20
rsocioecon age = 0.8
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Copulas
Clayton (rank=0.8) Gumbel (rank=0.8) Diagonal band (rank=0.8)
Normal (rank=0.8) Student’s T, degree 1 (rank=0.8)
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r socioecon age
(rank correlation)
normal copula
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r socioecon age
(rank correlation)
normal copula
r socioecon age
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r socioecon age
(rank correlation)
normal copula
r socioecon age
❶
❷
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r socioecon age
(rank correlation)
normal copula
r socioecon age
r cancerrisk socioecon
r cancerrisk age | socioecon
(conditional rank correlation)
❶
❷
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Continuous Non-Parametric Bayesian Network
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Uninet walkthrough
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UninetEngine.dll
C++ C# Delphi VB.net
MATLAB R Octave VBA (Excel)
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• The UninetEngine COM library is an extensive, object oriented, language-independent library: over seventy classes, over 500 methods (functions) • There are different Bayes net samplers accessible through the programmatic interface (e.g. the pure memory sampler used by UoM)
• There are a number of extra facilities accessible through the programmatic interface (e.g. a Bayes net can be specified via a product-moment correlation matrix) • Uninet is free for academic use
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Examples of NPBN projects with Uninet Risk analysis applications
• Earth dams safety in the State of Mexico • Linking PM2.5 concentrations to stationary source emissions • Causal models for air transport safety (CATS) • The benefit-risk analysis of food consumption (BENERIS) • The human damage in building fire • Platypus: Shell (risk analysis for chemical process plants)
Reliability of structures • Bayesian network for the weigh in motion system of the Netherlands (WIM)
Properties of materials • Technique for probabilistic multi-scale modelling of materials
Dynamic NPBNs • Permeability field estimation • Traffic prediction in the Netherlands
Ongoing • Filtration techniques (wastewater treatment plants) • Flood defences • Train disruptions • National Institute for Aerospace, Virginia USA: BbnSculptor • Wildfire Regime Simulators for UniMelb (FROST)
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CATS
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BENERIS
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Human Damage in Building Fire
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WIM
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printf("thank you!");
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References: For examples of major projects mentioned in this talk which are using/have used NPBNs in Uninet: • Ale, B., Bellamy, L., Cooke R.M., Duyvis, M., Kurowicka, D., Lin, P., et al. (2008) Causal model for air transport safety. Final Rep. ISBN 10: 90 369 1724-7,
Ministerie van Verkeer en Waterstaat • Ale, B., Bellamy, L., Cooper, J., Ababei, D., Kurowicka, D., Morales-Napoles, O., et al. (2010) Analysis of the crash of TK 1951 using CATS. Reliability
Engineering and System Safety, 95: 469–477 • Jesionek, P., Cooke, R. (2007) Generalized method for modelling dose–response relations—application to BENERIS project. Technical report. European
Union project • D. Hanea, D., Jagtman, H., Ale B. (2012) Analysis of the Schiphol cell complex fire using a Bayesian belief net based model. Reliability Engineering and
System Safety, 100: 115–124 • Morales-Nápoles, O., Steenbergen R. (2014) Analysis of axle and vehicle load properties through Bayesian networks based on weigh-in-motion data,
Reliability Engineering and System Safety, 125: 153–164 • Morales-Nápoles, O., Steenbergen, R. (2015) Large-scale hybrid Bayesian network for traffic load modelling from weigh-in-motion system data.
Journal of Bridge Eng ASCE, accepted for publication, 2015. For (other) examples of major projects which are using/have used NPBNs in Uninet, see the following synthesis paper and the references therein: • Hanea, A.M., Morales-Napoles, O., Ababei, D. (2015) Non-parametric Bayesian networks: Improving theory and reviewing applications. Reliability
Engineering & System Safety, 144: 265–284
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References:
For further exploring NPBNs, see: • Kurowicka, D. , Cooke, R.M. (2011) Vines and continuous non-parametric Bayesian belief nets, with emphasis on model learning, Ch. 24 in Klaus
Boecker (ed.) Re-Thinking Risk Measurement and Reporting, Uncertainty, Bayesian Analysis and Expert Judgement, pp 273-294, Risk Books, London • Hanea, A.M., Kurowicka, D., Cooke, R.M., Ababei, D. (2010) Mining and visualising ordinal data with non-parametric continuous BBNs. Computational
Statistics and Data Analysis, 54(3): 668-687 • Cooke, R.M., Hanea, A.M., Kurowicka, D. (2007) Continuous/Discrete Non Parametric Bayesian Belief Nets with UNICORN and UNINET, In Proceedings
of Mathematical Methods in Reliability, Glasgow, Scotland. • Hanea, A.M., Kurowicka, D., Cooke, R.M. (2006) Hybrid method for quantifying and analyzing Bayesian belief nets. Quality and Reliability Engineering
International 22(6): 709-729