Transcript Slide 1

Probabilistic Approach to
Design under Uncertainty
Dr. Wei Chen
Associate Professor
Integrated DEsign Automation Laboratory
(IDEAL)
Department of Mechanical Engineering
Northwestern University
Outline
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Uncertainty in model-based design
What is probability theory?
How does one represent uncertainty?
What is the inference mechanism?
Connection between probability theory and
utility theory
• Dealing with various sources of uncertainty
in model-based design
• Summary
Types of Uncertainty in Model-Based Design
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Model (lack of knowledge)
Parametric (lack of knowledge, variability)
Numerical
Testing data
Problem faced in design under uncertainty
• To choose from among one set of possible design
options X, where each involves a range of uncertain
outcomes Y
• To avoid making an “illogical choice”
Basic Concepts of Probability Theory
• Probability theory is the mathematical study of probability.
• Probability derives from fundamental concepts of set theory and
measurement theory.
Sample Space 
e2
e3
Event A
e1
e4
Example: Flip two coins
Sample space  – set of all
possible outcomes of a random
experiment under uncertainty
Outcomes {e1=HH, e2=HT,
e3=TH, e4=TT}
Event –subset of a sample space
e.g., A {e2 and e3} –experiments result in two different faces
Probability P(e1)=P(e2)=P(e3)=P(e4)=0.25
P(null) = 0
P()=1
P(A)= P(e2)+P(e3)=0.5
Mathematics in Probability Theory
• Three axioms of probability measure
– 0  P(A) 1; P()=1; P(Ai)=P(Ai) Ai are disjoint events
• Arithmetic of probabilities
– Union, Intersection, and Conditional probabilities
• Random variable is a function that assigns a real
number to each outcome in the sample space
Example: define x = total number of heads among the two tosses
Possible values {X=0}={TT}; {X=1}={HT, TH}, {X=2}={HH}
P{X=1}=0.5
• Probability density function & arithmetic of moments
of a random variable, e.g.,
E[XY]=E[X]E[Y] if X and Y are independent
• Convergence (law of large numbers) and central limit
theorem
Probabilistic Design Metrics in Quality Engineering
Robustness
Probability
Density (pdf)
Target M
sy
R=Area = Prob{g(x)c}
pdf
Bias
0
Reliability
my
sy Performance y
Minimizing the effect of variations
without eliminating the causes
C
Performance g
To assure proper levels of
“safety” for the system designed
Philosophies of Estimating Probability
• Frequentist
– Assign probabilities only to events that are random based on
outcomes of actual or theoretical experiments
– Suitable for problems with well-defined random experiments
• Bayesian
– Assign probabilities to propositions that are uncertain according to
subjective or logically justifiable degrees of belief in their truth
Example of proposition: “there was life on Mars a billion years
ago”
– More suitable for design problems: events in the future, not in the
past; all design models are predictive.
– More popular among decision theorists
Bayesian Inference
• In the absence of data (experiments), we have to guess
– A probability guess relies on our experience with “related”
events
• Once data is collected, inference relies on Bayes
theorem
– Probabilities are always personal degrees of belief
– Probabilities are always conditional on the information currently
available
– Probabilities are always subjective
• “Uncertainty of probability” is not meaningful.
Bernardo, J.M. and Smith, A. F., Bayesian Theory,
John Wiley, New York, 2000.
Bayes’ Theorem
P (H)
P( D | H ) P( H )
P( H | D) 
P( D)
Prior mean
H
Belief about H before
obtaining data, prior P
H - Hypothesis
D - Data
P (D | H) = L(H)
Updated
by data
Max. Likelihood. Est.
Data
P (H | D)
Posterior mean
H
H
Belief about H after
obtaining data, posterior P
Bayes’ theorem provides
•A solution to the problem of how to learn from data
•A form of uncertainty accounting
•A subjective view of probability
Formalism of Bayesian Statistics
• Offers a rationalist theory of personalistic beliefs in
contexts of uncertainty with axioms clearly stated
• Establishes that expected utility maximization provides
the basis for rational decision making
• Not descriptive, i.e., not to model actual behavior.
• Prescriptive, i.e., how one should act to avoid
undesirable behavioural inconsistency
Connection of Probability Theory and Utility Theory
• Three basic elements of decision
– the alternatives (options) X
– the predicted outcomes (performance) Y
– decision maker’s preference over the outcomes, expressed as an
objective function f in optimization
• Utility theory
– Utility is a preference function built on the axiomatic basis
originally developed by von Neumann and Morgenstern (1947)
– Six axioms (Luce and Raiffa, 1957; Thurston, 2006)

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Completeness of complete order
Transitivity
Monotonicity
Probabilities exist and can be quantified
Monotonicity of Probability
Substitution-independence
In agreement to
employing
probability to
model uncertainty
Decision Making – Ranking Design Alternatives
• Without uncertainty
– objective function f = V(Y) = V(Y(X))
V - value function, e.g. profit
• With uncertainty
– objective function f = E(U )   U ( V ) pdf ( V )dV
E(U) - expected utility. The preferred choice is the alternative
(lottery) that has the higher expected utility.
pdf (V)
U (V)
Risk averse
1
Risk neutral
Risk prone
A
B
V (e.g. profit)
0
worst
best
V
Issues in Model-Based Design
• How should we provide probabilistic quantification of
uncertainty associated with a model?
• How should we deal with model uncertainty (reducible)
and parameter uncertainty (irreducible) simultaneously?
• How should we make a design decision with good
confidence?
Chen, W., Xiong, Y., Tsui, K-L., and Wang, S., “Some Metrics and a
Bayesian Procedure for Validating Predictive Models in Engineering
Design”, DETC2006-99599, ASME Design Technical Conference.
Bayesian Approach for Quantifying the Uncertainty of
Predictive Model
Y ( x )  Y ( x )   ( x)
e
r
 Y m (x)   (x)   (x)
Y e (x)
Y r (x)
Y m (x)
 ( x)
 ( x)
- Physical observation
- True but unknown real performance
- Computer model output
m
- Bias function (between Y r (x) and Y (x))
- Random error in physical experiment
Bayesian Approach
[Y e (x)  Y m (x)]   (x)   (x)
ˆ(x) and UQ
Bias-Correction
Y r (x)  Y m (x)   (x)
Yˆ r (x) and UQ
m
Yˆ m (x) Metamodel of Y (x)
Computer experiments
Physical experiments
Observations (data) of  (x)
Bayesian posterior
of  (x)
Uncertainty is
accounted for by  (x)
More about the Bayesian Approach
Model assumption
 (x) - Gaussian process
.
p
R (xi  x j )   exp   k ( xik  x j k )2  
mean: m (x)  f (x) covariance: s  R
k 1
 (x) - Gaussian process (I.I.D.)
mean: zero variance: s 2  s 2
Priors distribution of parameters (nondeterministic)
T
  s2
N (b s2V ) s2
2
IG(    )
Data
IG(    )
ye  ( ye (x1 )  ye (xne ))T
Physical experiment
Computer experiment
s 2
ym  ( ym (x1 )  ym (xne ))T
m
m
m
T
or y  (Yˆ (x1 )  Yˆ (xne ))
δne  ye (x1 )  ym (x1 )
Known parameters (deterministic)
 k  Estimated from data, by MLE or Cross validation
Posterior distribution of parameters  s 2 (omitted here)
Posterior distribution of  (x)
 (x)  ye  ym   T (n em  m em (x) s2em (x))
That is, the posterior of  (x) is a
non-central t process
 ye (xne )  ym (xne ))T
Integrated Framework for Handling Model and
Parameter Uncertainties
Given
computer model
Sequential
experiment design
Physical
Computer
experiments experiments
Parameter
uncertainty
Specified
confidence
level Pth
Predictive model Yˆ r ( x)
and uncertainty quantification
Design objective function fˆ (x)
and uncertainty quantification
Design validation metrics M D
Design validity
requirements satisfied
(MD < Pth)?
No
Yes
Design decision
Expected Utility Optimization
Uncertainty Quantification of Design Objective
Function with Parameter Uncertainty
A robust design objective
x
f ( x)
(smaller-is-better) is used to determine the optimal solutions.
f (x)  w1  mY r (x)  w2  sY r (x)
x is a design variable
and a noise variable
w1, w2 : weighting factors
Uncertainty of
Uncertainty of f (x)
Y r (x)
Apley et al. (2005) developed analytical formulations to approximately quantify the mean
and variance of f (x) . In this example, Monte Carlo Simulation is employed.
Y r (x)
f ( x)
58.5
58.5
f ( x)
58
Yrpredction
realization of Y r
95% PI
57.5
57
57
56.5
56.5
56
f
y
57.5
fpredction
58
95% PI
55.5
56
55.5
55
55
54.5
54.5
54
54
53.5
53.5
0
0.1
0.2
0.3
0.4
0.5
x
0.6
0.7
0.8
0.9
r
Realizations of Y (x)
1
0
0.1
0.2
0.3
0.4
0.5
x
0.6
Mean of
95% PI
0.7
0.8
f ( x)
0.9
1
Validation Metrics
Probabilistic measure of whether a candidate optimal design is better
than other design choices with respect to a particular design objective
m f  2s f
mf
m f  2s f
f
m f  2s f
mf
m f  2s f
f
x
x1
x*
x
x1
x2
Smaller confidence
x*
x2
Larger confidence
Three types of design validation metrics (MD) – f is small-the-better
1
 Type 1: Multiplicative Metric
 Type 2: Additive Metric
 Type 3: Worst-Case Metric




M D (x*)    P  f (x*)  f (xi )


xi d , xi X 0

1
M D (x*) 
 P  f (x*)  f (xi )
N xi d , xi X 0
M D (x*) 
min
xi d , xi X 0
K
averaging
P  f (x*)  f (xi )
MD is intended to quantify the confidence of choosing x* as the optimal design
among all design candidates or within design region  d .
Summary
• Prediction is the basis for all decision making, including
engineering design.
• Probability is a belief (subjective), while observed frequencies
are used as evidence to update the belief.
• Probability theory and the Bayes theorem provide a rigorous
and philosophically sound framework for decision making.
• Predictive models in design should be described as stochastic
models.
• The impact of model uncertainty and parameter uncertainty can
be treated separately in the process of improving the predictive
capability.
• Probabilistic approach offers computational advantages and
mathematical flexibility.