1 a comparison of information management using imprecise probabilities and precise bayesian updating...

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A Comparison of Information Management using Imprecise Probabilities and Precise Bayesian Updating of Reliability Estimates

Jason Matthew Aughenbaugh, Ph.D.jason@arlut.utexas.edu

Applied Research LaboratoriesUniversity of Texas at Austin

Jeffrey W. Herrmann, Ph.D.jwh2@umd.edu

Department of Mechanical Engineering and Institute for Systems ResearchUniversity of Maryland

Third International Workshop on Reliable Engineering Computing, NSF Workshop on Imprecise Probability in Engineering Analysis &

Design, Savannah, Georgia, February 20-22, 2008.

2

Motivation

• Need to estimate reliability of system with components of uncertain reliability.

• Which components should we test to reduce uncertainty about system reliability?

AB

C

3

Introduction

Data

Existing informationIs it relevant?

Is it accurate?

Prior characterization

Updated / posteriorcharacterization

New experiments

Statisticalmodeling and updating

approach

-3 -2 -1 0 1 2 30

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-3 -2 -1 0 1 2 30

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-3 -2 -1 0 1 2 30

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-3 -2 -1 0 1 2 30

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Statistical Approaches

• Compare the following approaches: (Precise) Bayesian Robust Bayesian

• sensitivity analysis of prior

Imprecise probabilities• actual “true” probability is imprecise• the imprecise beta model

}Different philosophicalmotivations, but

equivalent math. forthis problem

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Is precise probability sufficient?

• Problem: equiprobable Know nothing or know they are equally likely?

• Why does it matter? Engineer A states that input values 1 and 2 have equal

probabilities Engineer B is designing a component that is very

sensitive to this input Should Engineer B proceed with a costly but versatile

design, or study the problem further?• Case 1: Engineer A had no idea, so stated equal. Study =good• Case 2: Engineer A performed substantial analysis. Additional

study = wasteful.

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Moving beyond precise probability

• Start with well established principles and mathematics Conclude it is insufficient

• Abandon probability completely?

• Relax conditions, extend applicability?

Think sensitivity analysis. How much do deviations from a precise prior matter?

7

Robust Bayes, Imprecise Beta Model

• Instead of one prior, consider many (a set)

1 (1 ) 1,

00

0

Conjugate model:

Beta Model parameterized with and :

( ) (1 )

Prior knowledge:

[ , ] prior estimate of mean

prior "sample size"

Experiment: observe failures in

st s ts t

s t

t t

s

m n

0 00

0 0 0

0

0

trials.

Update:

min{( ) /( )}

max ( ) /( )

n

n

n

n

t s t m s n

t s t m s n

s s n

s s n

Cum

ulat

ive

Pro

babi

lity

θ

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1

alpha = 5, beta = 95alpha = 10, beta = 90alpha = 15, beta = 85

0 00.05, 100t s 0 00.10, 100t s 0 00.15, 100t s

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Problem Description

• A simple parallel-series system, some info

• Assume we can test 12 more components How should these tests be allocated? A single test plan can have different outcomes

• Compare different scenarios of existing information

AB

C

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Multiple Outcomes of Experiement

• Precise probability Consider one outcome: test A 12 times, 2 fail

• Get one new posterior; precise parameters

Consider all possible outcomes: test A, get…• Get a new posterior for each possible outcome;

sets of parameters

• Imprecise probability One outcome, one SET of posteriors Multiple outcomes, SET of SETS of posteriors

How measure uncertainty? How make comparisons and decisions?

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Metrics of Uncertainty: Precise Distributions

• Variance-based sensitivity analysis (SVi) • (Sobol, 1993; Chan et al., 2000)

variance of the conditional expectation / total variance focuses on status quo, next (local) piece of info testing a component with a large sensitivity analysis

should reduce variance of system reliability estimate

• Mean and variance observations• Posterior variance

11Metrics of Uncertainty: Imprecise Distributions

• Imprecise variance-based sensitivity analysis (Hall, 2006) Does not worry about outcomes; local metric

• Mean and variance dispersion

• Imprecision in the mean

• Imprecision in the variance

,

,

min

max

i pip F

i i pp F

SV SV

SV SV

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Scenarios with Precise Distributions

• Components have beta distributions for the prior distributions of failure probability

• Scenario 1 System failure probability:

mean = 0.2201 variance = 0.0203

• Scenario 2 System failure probability:

mean = 0.1691variance = 0.0116

A B C

0

0

0.15

10

t

s

0

0

0.15

2

t

s

0

0

0.55

10

t

s

Scenario 1 priors

A B C

Scenario 2 priors

0

0

0.15

10

t

s

0

0

0.15

2

t

s

0

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10

t

s

AB

CXX

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Scenario 1 Results

• Variance-based sensitivity analysis:

0.4814

0.4583

0.0181

A

B

C

SV

SV

SV

• Posterior variance:

Table 1. Posterior variance for scenario 1 Posterior Variance Across Test Results Test Plan

#:{ , , }A B Cn n n Min Max

1:{12,0,0} 0.0110 0.0151

2:{0,12,0} 0.0117 0.0175

3:{0,0,12} 0.0131 0.0291

4:{4,4,4} 0.0071 0.0195

5:{6,6,0} 0.0059 0.0181

6:{6,0,6} 0.0094 0.0228

7:{0,6,6} 0.0117 0.0177

Best worst-case

Best best-case

AB

C

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Scenario 1 Results

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.005

0.01

0.015

0.02

0.025

mean

varia

nce

Test plan 1: [12, 0, 0]

Test plan 2: [0, 12, 0]Test plan 3: [0, 0, 12]

Test plan 4: [4, 4, 4]

Test plan 5: [6, 6, 0]

Test plan 6: [6, 0, 6]Test plan 7: [0, 6, 6]

Prior

AB

C

0.4814

0.4583

0.0181

A

B

C

SV

SV

SV

1

2

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Scenario 2 Results

• Variance-based sensitivity analysis:

• Posterior variance:

0.8982

0.0560

0.0153

A

B

C

SV

SV

SV

Table 1. Posterior variance for scenario 2 Posterior Variance Across Test Results Test Plan

#:{ , , }A B Cn n n Min Max

1:{12,0,0} 0.0042 0.0109

2:{0,12,0} 0.0115 0.0155

3:{0,0,12} 0.0116 0.0218

4:{4,4,4} 0.0064 0.0158

5:{6,6,0} 0.0051 0.0145

6:{6,0,6} 0.0054 0.0160

7:{0,6,6} 0.0115 0.0145

AB

C

Best worst-case

Best best-case

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Scenario 2 Results

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.005

0.01

0.015

0.02

0.025

mean

varia

nce

Test plan 1: [12, 0, 0]

Test plan 2: [0, 12, 0]Test plan 3: [0, 0, 12]

Test plan 4: [4, 4, 4]

Test plan 5: [6, 6, 0]

Test plan 6: [6, 0, 6]Test plan 7: [0, 6, 6]

Prior

AB

C

0.8982

0.0560

0.0153

A

B

C

SV

SV

SV

1

2

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Scenario 3: Imprecise Distributions

• Component failure probabilities are modeled using imprecise beta distributions

• System failure probability an imprecise distribution: Mean: 0.2201 to 0.4640 Variance: 0.0136 to 0.0332

• Imprecise variance-based sensitivity analysis:

A B C

0

0

0

0

0.15

0.20

10

12

t

t

s

s

0

0

0

0

0.15

0.55

2

5

t

t

s

s

0

0

0

0

0.55

0.60

10

12

t

t

s

s

0.1363 to 0.7204

0.2406 to 0.6960

0.0116 to 0.2512

A

B

C

SV

SV

SV

Since failure probability of B is poorly known,

we allow for a range.

Scenario 3 comparable to precise scenario 1.

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Posterior Variance Analysis

Smallest variances, and smallest imprecision in variances.

0.1363 to 0.7204

0.2406 to 0.6960

0.0116 to 0.2512

A

B

C

SV

SV

SV

Table 1. Posterior variance analysis for scenario 3

V Imprecision in V Test Design

#:{ , , }A B Cn n n Minimum minimum

Maximum maximum

Minimum average

Maximum average

Minimum Maximum

Prior 0.0136 0.0332 n.a. n.a. 0.0196

1:{12,0,0} 0.0075 0.0344 0.0094 0.0304 0.0046 0.0259

2:{0,12,0} 0.0099 0.0181 0.0103 0.0153 0.0035 0.0051

3:{0,0,12} 0.0103 0.0465 0.0134 0.0310 0.0070 0.0293

4:{4,4,4} 0.0059 0.0162 0.0075 0.0118 0.0020 0.0054

5:{6,6,0} 0.0056 0.0189 0.0083 0.0150 0.0022 0.0063

6:{6,0,6} 0.0068 0.0458 0.0107 0.0295 0.0041 0.0309

7:{0,6,6} 0.0100 0.0183 0.0109 0.0183 0.0026 0.0060

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Results for Scenario 3

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.005

0.01

0.015

0.02

0.025

0.03

0.035

mean

varia

nce

Test plan 1: [12, 0, 0]

Test plan 2: [0, 12, 0]

Test plan 5: [6, 6, 0]

Prior

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.005

0.01

0.015

0.02

0.025

0.03

0.035

mean

varia

nce

Test plan 3: [0, 0, 12]

Test plan 6: [6, 0, 6]

Test plan 7: [0, 6, 6]

Prior

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.005

0.01

0.015

0.02

0.025

0.03

0.035

mean

varia

nce

Test plan 2: [0, 12, 0]

Test plan 4: [4, 4, 4]

Test plan 5: [6, 6, 0]Test plan 7: [0, 6, 6]

Prior

Sample results:[12, 0, 0], [0, 12, 0], [6, 6, 0]

Convex hull of results: [12, 0, 0], [0, 12, 0], [6, 6, 0]

Convex hull of results:[0, 0, 12], [6, 0, 6], [0, 6, 6]

Convex hull of results:[0, 12, 0], [4, 4, 4], [6, 6, 0], [0, 6, 6]

0.1363 to 0.7204

0.2406 to 0.6960

0.0116 to 0.2512

A

B

C

SV

SV

SV

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Scenario 4: Imprecise Distributions

• Component failure probabilities are modeled using imprecise beta distributions

• System failure probability is also an imprecise distribution: Mean: 0.1691 to 0.2880 Variance: 0.0100 to 0.0173

• Imprecise variance-based sensitivity analysis:

A B C

0

0

0

0

0.15

0.20

10

12

t

t

s

s

0

0

0

0

0.15

0.55

2

5

t

t

s

s

0

0

0

0

0.15

0.20

10

12

t

t

s

s

0.5438 to 0.9590

0.0210 to 0.1819

0.0095 to 0.2515

A

B

C

SV

SV

SV

Compared to scenario 3,the failure probability of C

is reduced.

This makes it comparable to precise scenario 2.

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Results for Scenario 4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.005

0.01

0.015

0.02

0.025

0.03

0.035

mean

varia

nce

Test plan 1: [12, 0, 0]

Test plan 2: [0, 12, 0]

Test plan 5: [6, 6, 0]

Prior

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.005

0.01

0.015

0.02

0.025

0.03

0.035

mean

varia

nce

Test plan 3: [0, 0, 12]

Test plan 6: [6, 0, 6]

Test plan 7: [0, 6, 6]

Prior

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.005

0.01

0.015

0.02

0.025

0.03

0.035

mean

varia

nce

Test plan 1: [12, 0, 0]

Test plan 4: [4, 4, 4]

Test plan 7: [0, 6, 6]

Prior

Convex hull of results:[12, 0, 0], [0, 12, 0], [6, 6, 0]

Convex hull of results:[0, 0, 12], [6, 0, 6], [0, 6, 6]

Convex hull of results:[12, 0, 0], [4, 4, 4], [0, 6, 6]

0.5438 to 0.9590

0.0210 to 0.1819

0.0095 to 0.2515

A

B

C

SV

SV

SV

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Discussion / Future Work

• Multiple sources of uncertainty Existing knowledge Results of future tests

• How do we prioritize different aspects? Variance or imprecision reduction? Best case, worst case, average case of results? Incorporate economic/utility metrics?

• Other imprecision/total uncertainty measures? “Breadth” of p-boxes (Ferson and Tucker, 2006 ) Aggregate uncertainty, others(Klir and Smith, 2001)

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Summary

• Shown how to use different statistical approaches for evaluating experimental test plans

• Used direct uncertainty metrics Variance-based sensitivity analysis

• Precise and imprecise Posterior variance Dispersion of the mean and variance Imprecision in the mean and variance

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Thank you for your attention.

• Questions? Comments? Discussion?

This work supported in part by the Applied Research Laboratories at UT-Austin Internal IR&D grant 07-09

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SVi

2

2 2

2 2

11

11

11

A B C A

B C A B

C B A C

SV E P E P V PV

SV E P E P V PV

SV E P E P V PV

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Formulae

/A A A AE P

;

2

1A A

A

A A A A

V P

;

22 1

1A A

A AAA A A A

E P V P E P

.The mathematical model for the reliability of the system shown in Figure 1 follows.

1 (1 )(1 )sys A B CR R R R

sys A B C A B CP P P P P P P

[ ] [ ] [ ] [ ] [ ] [ ] [ ]A B C A B CE E P E P E P E P E P E P

2 2 2

2 2 2 2 2 2 2

[ ] [ ] 2 [ ] [ ] [ ] 2 [ ] [ ] [ ]

[ ] [ ] 2 [ ] [ ] [ ] [ ] [ ] [ ]A A B C A B C

B C A B C A B C

E E P E P E P E P E P E P E P

E P E P E P E P E P E P E P E P

2 2[ ] [ ] ( [ ])V E E