Markov chain Monte Carlo Revolution in Reliability Engineering Konstantin Zuev Department of Mathematics University of Southern California http://www-bcf.usc.edu/∼kzuev December 3, 2011 Southern California Probability Symposium Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 1 / 26
95
Embed
Markov chain Monte Carlo Revolution in Reliability …zuev/talks/MCMC_rev.pdfKonstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 2 / 26 MCMC Revolution P. Diaconis
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Markov chain Monte Carlo Revolution
in Reliability Engineering
Konstantin Zuev
Department of Mathematics
University of Southern California
http://www-bcf.usc.edu/∼kzuev
December 3, 2011
Southern California Probability Symposium
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 1 / 26
MCMC Revolution
P. Diaconis (2009), “The Markov chain Monte Carlo revolution”:
...asking about applications of Markov chain Monte Carlo (MCMC)
is a little like asking about applications of the quadratic formula...
you can take any area of science, from hard to social, and find a
burgeoning MCMC literature specifically tailored to that area.
Statistics: Byesian inference
Statistical Physics: sampling from the Boltzman distribution
Biochemistry: protein structure simulation
Astronomy: hypothesis testing for astronomical observations
Linguistics: linguistic data analysis
The main goal of this talk: To show how MCMC can be efficiently used for
solving problems in Reliability Engineering
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 2 / 26
MCMC Revolution
P. Diaconis (2009), “The Markov chain Monte Carlo revolution”:
...asking about applications of Markov chain Monte Carlo (MCMC)
is a little like asking about applications of the quadratic formula...
you can take any area of science, from hard to social, and find a
burgeoning MCMC literature specifically tailored to that area.
Statistics: Byesian inference
Statistical Physics: sampling from the Boltzman distribution
Biochemistry: protein structure simulation
Astronomy: hypothesis testing for astronomical observations
Linguistics: linguistic data analysis
The main goal of this talk: To show how MCMC can be efficiently used for
solving problems in Reliability Engineering
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 2 / 26
MCMC Revolution
P. Diaconis (2009), “The Markov chain Monte Carlo revolution”:
...asking about applications of Markov chain Monte Carlo (MCMC)
is a little like asking about applications of the quadratic formula...
you can take any area of science, from hard to social, and find a
burgeoning MCMC literature specifically tailored to that area.
Statistics: Byesian inference
Statistical Physics: sampling from the Boltzman distribution
Biochemistry: protein structure simulation
Astronomy: hypothesis testing for astronomical observations
Linguistics: linguistic data analysis
The main goal of this talk: To show how MCMC can be efficiently used for
solving problems in Reliability Engineering
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 2 / 26
Outline
1 Reliability Problem
2 Pre-MCMC era
3 First MCMC pancake
4 Subset Simulation
5 Enhancements for Subset Simulation
6 Summary
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 3 / 26
Outline
1 Reliability Problem
2 Pre-MCMC era
3 First MCMC pancake
4 Subset Simulation
5 Enhancements for Subset Simulation
6 Summary
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 3 / 26
Reliability Problem
Reliability Problem: To estimate the probability of failure pF
pF = P (x ∈ F ) =
∫Rd
π(x)IF (x)dx
Notation:
x ∈ Rd represents the uncertain excitation of a system
I x is a random vector with joint PDF π(x) (multivariate standard normal)
F ⊂ Rd a failure domain (unacceptable system performance)
F = {x : g(x) ≥ b∗}
g(x) a performance function (loss function)
b∗ a critical threshold for performance
IF (x) = 1 if x ∈ F and IF (x) = 0 if x /∈ F
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 4 / 26
Reliability Problem
Reliability Problem: To estimate the probability of failure pF
pF = P (x ∈ F ) =
∫Rd
π(x)IF (x)dx
Notation:
x ∈ Rd represents the uncertain excitation of a system
I x is a random vector with joint PDF π(x) (multivariate standard normal)
F ⊂ Rd a failure domain (unacceptable system performance)
F = {x : g(x) ≥ b∗}
g(x) a performance function (loss function)
b∗ a critical threshold for performance
IF (x) = 1 if x ∈ F and IF (x) = 0 if x /∈ F
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 4 / 26
Why is this problem computationally challenging?
pF =
∫Rd
π(x)IF (x)dx, F = {x : g(x) ≥ b∗}
Typically in Applications:
The relationship between x and IF (x) is not explicitly known
We can compute IF (x) for any x, but this computation is expensive
The probability of failure pF is very small, pF ∼ 10−2 − 10−9
The dimension d is very large, d ∼ 103
Consequences:
Numerical integration is not suitable
Standard Monte Carlo is computationally infeasible
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 5 / 26
Why is this problem computationally challenging?
pF =
∫Rd
π(x)IF (x)dx, F = {x : g(x) ≥ b∗}
Typically in Applications:
The relationship between x and IF (x) is not explicitly known
We can compute IF (x) for any x, but this computation is expensive
The probability of failure pF is very small, pF ∼ 10−2 − 10−9
The dimension d is very large, d ∼ 103
Consequences:
Numerical integration is not suitable
Standard Monte Carlo is computationally infeasible
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 5 / 26
Why is this problem computationally challenging?
pF =
∫Rd
π(x)IF (x)dx, F = {x : g(x) ≥ b∗}
Typically in Applications:
The relationship between x and IF (x) is not explicitly known
We can compute IF (x) for any x, but this computation is expensive
The probability of failure pF is very small, pF ∼ 10−2 − 10−9
The dimension d is very large, d ∼ 103
Consequences:
Numerical integration is not suitable
Standard Monte Carlo is computationally infeasible
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 5 / 26
Why is this problem computationally challenging?
pF =
∫Rd
π(x)IF (x)dx, F = {x : g(x) ≥ b∗}
Typically in Applications:
The relationship between x and IF (x) is not explicitly known
We can compute IF (x) for any x, but this computation is expensive
The probability of failure pF is very small, pF ∼ 10−2 − 10−9
The dimension d is very large, d ∼ 103
Consequences:
Numerical integration is not suitable
Standard Monte Carlo is computationally infeasible
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 5 / 26
Why is this problem computationally challenging?
pF =
∫Rd
π(x)IF (x)dx, F = {x : g(x) ≥ b∗}
Typically in Applications:
The relationship between x and IF (x) is not explicitly known
We can compute IF (x) for any x, but this computation is expensive
The probability of failure pF is very small, pF ∼ 10−2 − 10−9
The dimension d is very large, d ∼ 103
Consequences:
Numerical integration is not suitable
Standard Monte Carlo is computationally infeasible
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 5 / 26
Why is this problem computationally challenging?
pF =
∫Rd
π(x)IF (x)dx, F = {x : g(x) ≥ b∗}
Typically in Applications:
The relationship between x and IF (x) is not explicitly known
We can compute IF (x) for any x, but this computation is expensive
The probability of failure pF is very small, pF ∼ 10−2 − 10−9
The dimension d is very large, d ∼ 103
Consequences:
Numerical integration is not suitable
Standard Monte Carlo is computationally infeasible
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 5 / 26
Outline
1 Reliability Problem
2 Pre-MCMC era
I Approximate methods
I Simulation methods
3 First MCMC pancake
4 Subset Simulation
5 Enhancements for Subset Simulation
6 Summary
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 6 / 26
Pre-MCMC Era: Approximate Methods
FORM: First-Order Reliability Method
Failure domain F = {x : g(x) ≥ b∗}
Limit-state surface ∂F = {x : g(x) = b∗}
Design point x∗ = arg minx∈∂F
‖x‖
Reliability index β = ‖x∗‖
FORM estimate pF ≈ Φ(−β)
Main advantage:
If g(x) is linear, then FORM gives the exact result
Main drawbacks:
If g(x) is not linear, then FORM estimate may be very inaccurate
FORM does not give any measure of the error introduced by linearization
Verdict: Valdebenito et al (2010): “FORM has no scientific basis”
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 7 / 26
Pre-MCMC Era: Approximate Methods
FORM: First-Order Reliability Method
Failure domain F = {x : g(x) ≥ b∗}
Limit-state surface ∂F = {x : g(x) = b∗}
Design point x∗ = arg minx∈∂F
‖x‖
Reliability index β = ‖x∗‖
FORM estimate pF ≈ Φ(−β)
Main advantage:
If g(x) is linear, then FORM gives the exact result
Main drawbacks:
If g(x) is not linear, then FORM estimate may be very inaccurate
FORM does not give any measure of the error introduced by linearization
Verdict: Valdebenito et al (2010): “FORM has no scientific basis”
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 7 / 26
Pre-MCMC Era: Approximate Methods
FORM: First-Order Reliability Method
Failure domain F = {x : g(x) ≥ b∗}
Limit-state surface ∂F = {x : g(x) = b∗}
Design point x∗ = arg minx∈∂F
‖x‖
Reliability index β = ‖x∗‖
FORM estimate pF ≈ Φ(−β)
Main advantage:
If g(x) is linear, then FORM gives the exact result
Main drawbacks:
If g(x) is not linear, then FORM estimate may be very inaccurate
FORM does not give any measure of the error introduced by linearization
Verdict: Valdebenito et al (2010): “FORM has no scientific basis”
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 7 / 26
Pre-MCMC Era: Approximate Methods
FORM: First-Order Reliability Method
Failure domain F = {x : g(x) ≥ b∗}
Limit-state surface ∂F = {x : g(x) = b∗}
Design point x∗ = arg minx∈∂F
‖x‖
Reliability index β = ‖x∗‖
FORM estimate pF ≈ Φ(−β)
Main advantage:
If g(x) is linear, then FORM gives the exact result
Main drawbacks:
If g(x) is not linear, then FORM estimate may be very inaccurate
FORM does not give any measure of the error introduced by linearization
Verdict: Valdebenito et al (2010): “FORM has no scientific basis”
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 7 / 26
Pre-MCMC Era: Approximate Methods
FORM: First-Order Reliability Method
Failure domain F = {x : g(x) ≥ b∗}
Limit-state surface ∂F = {x : g(x) = b∗}
Design point x∗ = arg minx∈∂F
‖x‖
Reliability index β = ‖x∗‖
FORM estimate pF ≈ Φ(−β)
Main advantage:
If g(x) is linear, then FORM gives the exact result
Main drawbacks:
If g(x) is not linear, then FORM estimate may be very inaccurate
FORM does not give any measure of the error introduced by linearization
Verdict: Valdebenito et al (2010): “FORM has no scientific basis”
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 7 / 26
Pre-MCMC Era: Simulation Methods
Importance Sampling:
pF =
∫Rd
π(x)IF (x)dx =
∫Rd
π(x)
µ(x)IF (x)µ(x)dx = Eµ
[π(x)
µ(x)IF (x)
]
µ(x) is the importance sampling density (a.k.a. instrumental or trial density)
pF ≈ pF =1
N
N∑i=1
π(x(i))
µ(x(i))IF (x(i)), x(1), . . . , x(N) ∼ µ
If supp(µ) ⊇ F ∩ supp(π), then pF → pF a.s. when N →∞
If µ(x) is “good”, then IS is an efficient variance reduction technique
Optimal ISD µopt(x) = π(x|F ) = π(x)IF (x)pF
Main drawbacks:
When F ⊂ Rd is high-dimensional, finding a good ISD is very challenging
Importnace sampling suffers from the curse of dimensionality
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 8 / 26
Pre-MCMC Era: Simulation Methods
Importance Sampling:
pF =
∫Rd
π(x)IF (x)dx =
∫Rd
π(x)
µ(x)IF (x)µ(x)dx = Eµ
[π(x)
µ(x)IF (x)
]
µ(x) is the importance sampling density (a.k.a. instrumental or trial density)
pF ≈ pF =1
N
N∑i=1
π(x(i))
µ(x(i))IF (x(i)), x(1), . . . , x(N) ∼ µ
If supp(µ) ⊇ F ∩ supp(π), then pF → pF a.s. when N →∞
If µ(x) is “good”, then IS is an efficient variance reduction technique
Optimal ISD µopt(x) = π(x|F ) = π(x)IF (x)pF
Main drawbacks:
When F ⊂ Rd is high-dimensional, finding a good ISD is very challenging
Importnace sampling suffers from the curse of dimensionality
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 8 / 26
Pre-MCMC Era: Simulation Methods
Importance Sampling:
pF =
∫Rd
π(x)IF (x)dx =
∫Rd
π(x)
µ(x)IF (x)µ(x)dx = Eµ
[π(x)
µ(x)IF (x)
]
µ(x) is the importance sampling density (a.k.a. instrumental or trial density)
pF ≈ pF =1
N
N∑i=1
π(x(i))
µ(x(i))IF (x(i)), x(1), . . . , x(N) ∼ µ
If supp(µ) ⊇ F ∩ supp(π), then pF → pF a.s. when N →∞
If µ(x) is “good”, then IS is an efficient variance reduction technique
Optimal ISD µopt(x) = π(x|F ) = π(x)IF (x)pF
Main drawbacks:
When F ⊂ Rd is high-dimensional, finding a good ISD is very challenging
Importnace sampling suffers from the curse of dimensionality
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 8 / 26
Pre-MCMC Era: Simulation Methods
Importance Sampling:
pF =
∫Rd
π(x)IF (x)dx =
∫Rd
π(x)
µ(x)IF (x)µ(x)dx = Eµ
[π(x)
µ(x)IF (x)
]
µ(x) is the importance sampling density (a.k.a. instrumental or trial density)
pF ≈ pF =1
N
N∑i=1
π(x(i))
µ(x(i))IF (x(i)), x(1), . . . , x(N) ∼ µ
If supp(µ) ⊇ F ∩ supp(π), then pF → pF a.s. when N →∞
If µ(x) is “good”, then IS is an efficient variance reduction technique
Optimal ISD µopt(x) = π(x|F ) = π(x)IF (x)pF
Main drawbacks:
When F ⊂ Rd is high-dimensional, finding a good ISD is very challenging
Importnace sampling suffers from the curse of dimensionality
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 8 / 26
The first MCMC pancake
Au and Beck (1999): Importance Sampling using kernel density estimators
Key idea: to use x(1), . . . , x(M) ∼ π(x|F ) = µopt(x) to construct
µ(x|x(1), . . . , x(M)) ≈ µopt(x)
How to obtain x(1), . . . , x(M) ∼ µopt(x)?
I Rejection Sampling is extremely inefficient if pF is small
I Generate a Markov chain with the stationary distribution µopt(x)!
A sampling density estimator:
µ(x|x(1), . . . , x(M)) =1
M
M∑i=1
1
(ωλi)dN(x− x(i)
ωλi
)Main drawback:
To work well, µ(x|x(1), . . . , x(M)) ≈ µopt(x) must be a good approximation
⇒ M must be large and x(1), . . . , x(M) must populate F properly
⇒ the method is impractical
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 9 / 26
The first MCMC pancake
Au and Beck (1999): Importance Sampling using kernel density estimators
Key idea: to use x(1), . . . , x(M) ∼ π(x|F ) = µopt(x) to construct
µ(x|x(1), . . . , x(M)) ≈ µopt(x)
How to obtain x(1), . . . , x(M) ∼ µopt(x)?
I Rejection Sampling is extremely inefficient if pF is small
I Generate a Markov chain with the stationary distribution µopt(x)!
A sampling density estimator:
µ(x|x(1), . . . , x(M)) =1
M
M∑i=1
1
(ωλi)dN(x− x(i)
ωλi
)Main drawback:
To work well, µ(x|x(1), . . . , x(M)) ≈ µopt(x) must be a good approximation
⇒ M must be large and x(1), . . . , x(M) must populate F properly
⇒ the method is impractical
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 9 / 26
The first MCMC pancake
Au and Beck (1999): Importance Sampling using kernel density estimators
Key idea: to use x(1), . . . , x(M) ∼ π(x|F ) = µopt(x) to construct
µ(x|x(1), . . . , x(M)) ≈ µopt(x)
How to obtain x(1), . . . , x(M) ∼ µopt(x)?
I Rejection Sampling is extremely inefficient if pF is small
I Generate a Markov chain with the stationary distribution µopt(x)!
A sampling density estimator:
µ(x|x(1), . . . , x(M)) =1
M
M∑i=1
1
(ωλi)dN(x− x(i)
ωλi
)Main drawback:
To work well, µ(x|x(1), . . . , x(M)) ≈ µopt(x) must be a good approximation
⇒ M must be large and x(1), . . . , x(M) must populate F properly
⇒ the method is impractical
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 9 / 26
The first MCMC pancake
Au and Beck (1999): Importance Sampling using kernel density estimators
Key idea: to use x(1), . . . , x(M) ∼ π(x|F ) = µopt(x) to construct
µ(x|x(1), . . . , x(M)) ≈ µopt(x)
How to obtain x(1), . . . , x(M) ∼ µopt(x)?
I Rejection Sampling is extremely inefficient if pF is small
I Generate a Markov chain with the stationary distribution µopt(x)!
A sampling density estimator:
µ(x|x(1), . . . , x(M)) =1
M
M∑i=1
1
(ωλi)dN(x− x(i)
ωλi
)Main drawback:
To work well, µ(x|x(1), . . . , x(M)) ≈ µopt(x) must be a good approximation
⇒ M must be large and x(1), . . . , x(M) must populate F properly
⇒ the method is impractical
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 9 / 26
The first MCMC pancake
Au and Beck (1999): Importance Sampling using kernel density estimators
Key idea: to use x(1), . . . , x(M) ∼ π(x|F ) = µopt(x) to construct
µ(x|x(1), . . . , x(M)) ≈ µopt(x)
How to obtain x(1), . . . , x(M) ∼ µopt(x)?
I Rejection Sampling is extremely inefficient if pF is small
I Generate a Markov chain with the stationary distribution µopt(x)!
A sampling density estimator:
µ(x|x(1), . . . , x(M)) =1
M
M∑i=1
1
(ωλi)dN(x− x(i)
ωλi
)Main drawback:
To work well, µ(x|x(1), . . . , x(M)) ≈ µopt(x) must be a good approximation
⇒ M must be large and x(1), . . . , x(M) must populate F properly
⇒ the method is impractical
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 9 / 26
The first MCMC pancake
Au and Beck (1999): Importance Sampling using kernel density estimators
Key idea: to use x(1), . . . , x(M) ∼ π(x|F ) = µopt(x) to construct
µ(x|x(1), . . . , x(M)) ≈ µopt(x)
How to obtain x(1), . . . , x(M) ∼ µopt(x)?
I Rejection Sampling is extremely inefficient if pF is small
I Generate a Markov chain with the stationary distribution µopt(x)!
A sampling density estimator:
µ(x|x(1), . . . , x(M)) =1
M
M∑i=1
1
(ωλi)dN(x− x(i)
ωλi
)
Main drawback:
To work well, µ(x|x(1), . . . , x(M)) ≈ µopt(x) must be a good approximation
⇒ M must be large and x(1), . . . , x(M) must populate F properly
⇒ the method is impractical
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 9 / 26
The first MCMC pancake
Au and Beck (1999): Importance Sampling using kernel density estimators
Key idea: to use x(1), . . . , x(M) ∼ π(x|F ) = µopt(x) to construct
µ(x|x(1), . . . , x(M)) ≈ µopt(x)
How to obtain x(1), . . . , x(M) ∼ µopt(x)?
I Rejection Sampling is extremely inefficient if pF is small
I Generate a Markov chain with the stationary distribution µopt(x)!
A sampling density estimator:
µ(x|x(1), . . . , x(M)) =1
M
M∑i=1
1
(ωλi)dN(x− x(i)
ωλi
)Main drawback:
To work well, µ(x|x(1), . . . , x(M)) ≈ µopt(x) must be a good approximation
⇒ M must be large and x(1), . . . , x(M) must populate F properly
⇒ the method is impractical
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 9 / 26
The first efficient MCMC methodAu and Beck (2001): Subset Simulation
Rd = F0 ⊃ F1 ⊃ . . . ⊃ Fm = F
F = {x : g(x) ≥ b∗}
Fi = {x : g(x) ≥ b∗i }
b∗1 < b∗2 < . . . < b∗m = b∗
pF =
m−1∏k=0
P (Fk+1|Fk)
P (Fk+1|Fk) ≈ 1
N
N∑i=1
IFk+1(x
(i)k )
x(i)k ∼ π(x|Fk) =
π(x)IFk(x)
P (Fk)
How to sample from π(x|Fk)?
Use an appropriate MCMC alg
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 10 / 26
The first efficient MCMC methodAu and Beck (2001): Subset Simulation
Rd = F0 ⊃ F1 ⊃ . . . ⊃ Fm = F
F = {x : g(x) ≥ b∗}
Fi = {x : g(x) ≥ b∗i }
b∗1 < b∗2 < . . . < b∗m = b∗
pF =
m−1∏k=0
P (Fk+1|Fk)
P (Fk+1|Fk) ≈ 1
N
N∑i=1
IFk+1(x
(i)k )
x(i)k ∼ π(x|Fk) =
π(x)IFk(x)
P (Fk)
How to sample from π(x|Fk)?
Use an appropriate MCMC alg
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 10 / 26
The first efficient MCMC methodAu and Beck (2001): Subset Simulation
Rd = F0 ⊃ F1 ⊃ . . . ⊃ Fm = F
F = {x : g(x) ≥ b∗}
Fi = {x : g(x) ≥ b∗i }
b∗1 < b∗2 < . . . < b∗m = b∗
pF =
m−1∏k=0
P (Fk+1|Fk)
P (Fk+1|Fk) ≈ 1
N
N∑i=1
IFk+1(x
(i)k )
x(i)k ∼ π(x|Fk) =
π(x)IFk(x)
P (Fk)
How to sample from π(x|Fk)?
Use an appropriate MCMC alg
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 10 / 26
The first efficient MCMC methodAu and Beck (2001): Subset Simulation
Rd = F0 ⊃ F1 ⊃ . . . ⊃ Fm = F
F = {x : g(x) ≥ b∗}
Fi = {x : g(x) ≥ b∗i }
b∗1 < b∗2 < . . . < b∗m = b∗
pF =
m−1∏k=0
P (Fk+1|Fk)
P (Fk+1|Fk) ≈ 1
N
N∑i=1
IFk+1(x
(i)k )
x(i)k ∼ π(x|Fk) =
π(x)IFk(x)
P (Fk)
How to sample from π(x|Fk)?
Use an appropriate MCMC alg
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 10 / 26
The first efficient MCMC methodAu and Beck (2001): Subset Simulation
Rd = F0 ⊃ F1 ⊃ . . . ⊃ Fm = F
F = {x : g(x) ≥ b∗}
Fi = {x : g(x) ≥ b∗i }
b∗1 < b∗2 < . . . < b∗m = b∗
pF =
m−1∏k=0
P (Fk+1|Fk)
P (Fk+1|Fk) ≈ 1
N
N∑i=1
IFk+1(x
(i)k )
x(i)k ∼ π(x|Fk) =
π(x)IFk(x)
P (Fk)
How to sample from π(x|Fk)?
Use an appropriate MCMC alg
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 10 / 26
Sampling from π(x|Fk)Standard Metropolis-Hastings algorithm is not efficient in high dimensions
Modified Metropolis-Hastings algorithm: xn xn+1
Generate candidate state y
For each j = 1 . . . d:
I Simulate yj ∼ Sj(·|xjn)I Compute the acceptance probability
aj(xjn, yj) = min
{1,πj(y
j)
πj(xjn)
}I Accept/Reject yj
yj =
yj , with prob. aj(xjn, yj)
xjn, with prob. 1− aj(xjn, yj)
Accept/Reject y
xn+1 =
y, if y ∈ Fkxn, if y /∈ Fk
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 11 / 26
Sampling from π(x|Fk)Standard Metropolis-Hastings algorithm is not efficient in high dimensions
Modified Metropolis-Hastings algorithm: xn xn+1
Generate candidate state y
For each j = 1 . . . d:
I Simulate yj ∼ Sj(·|xjn)I Compute the acceptance probability
aj(xjn, yj) = min
{1,πj(y
j)
πj(xjn)
}I Accept/Reject yj
yj =
yj , with prob. aj(xjn, yj)
xjn, with prob. 1− aj(xjn, yj)
Accept/Reject y
xn+1 =
y, if y ∈ Fkxn, if y /∈ Fk
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 11 / 26
Statistical properties and efficiency of Subset Simulation
SS estimator : pF =
m−1∏k=0
(1
N
N∑i=1
IFk+1(x
(i)k )
), x
(i)k ∼ π(x|Fk)
Statistical properties:
pF is asymptotically unbiased and bias is O(1/N)
pF is consistent and its coefficient of variation δ = O(1/√N)
Efficiency:
What total number of samples is required to achieve a given accuracy in pF ?
Standard Monte Carlo: NT ∝ 1/pF
Subset Simulation: NT ∝ | log pF |r, where r ≤ 3
SS is very efficient when estimating small probabilities
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 12 / 26
Statistical properties and efficiency of Subset Simulation
SS estimator : pF =
m−1∏k=0
(1
N
N∑i=1
IFk+1(x
(i)k )
), x
(i)k ∼ π(x|Fk)
Statistical properties:
pF is asymptotically unbiased and bias is O(1/N)
pF is consistent and its coefficient of variation δ = O(1/√N)
Efficiency:
What total number of samples is required to achieve a given accuracy in pF ?
Standard Monte Carlo: NT ∝ 1/pF
Subset Simulation: NT ∝ | log pF |r, where r ≤ 3
SS is very efficient when estimating small probabilities
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 12 / 26
Statistical properties and efficiency of Subset Simulation
SS estimator : pF =
m−1∏k=0
(1
N
N∑i=1
IFk+1(x
(i)k )
), x
(i)k ∼ π(x|Fk)
Statistical properties:
pF is asymptotically unbiased and bias is O(1/N)
pF is consistent and its coefficient of variation δ = O(1/√N)
Efficiency:
What total number of samples is required to achieve a given accuracy in pF ?
Standard Monte Carlo: NT ∝ 1/pF
Subset Simulation: NT ∝ | log pF |r, where r ≤ 3
SS is very efficient when estimating small probabilities
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 12 / 26
Statistical properties and efficiency of Subset Simulation
SS estimator : pF =
m−1∏k=0
(1
N
N∑i=1
IFk+1(x
(i)k )
), x
(i)k ∼ π(x|Fk)
Statistical properties:
pF is asymptotically unbiased and bias is O(1/N)
pF is consistent and its coefficient of variation δ = O(1/√N)
Efficiency:
What total number of samples is required to achieve a given accuracy in pF ?
Standard Monte Carlo: NT ∝ 1/pF
Subset Simulation: NT ∝ | log pF |r, where r ≤ 3
SS is very efficient when estimating small probabilities
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 12 / 26
Statistical properties and efficiency of Subset Simulation
SS estimator : pF =
m−1∏k=0
(1
N
N∑i=1
IFk+1(x
(i)k )
), x
(i)k ∼ π(x|Fk)
Statistical properties:
pF is asymptotically unbiased and bias is O(1/N)
pF is consistent and its coefficient of variation δ = O(1/√N)
Efficiency:
What total number of samples is required to achieve a given accuracy in pF ?
Standard Monte Carlo: NT ∝ 1/pF
Subset Simulation: NT ∝ | log pF |r, where r ≤ 3
SS is very efficient when estimating small probabilities
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 12 / 26
Numerical Example
Linear Problem
I ∂F ⊂ Rd is a hyperplane
I d = 1000
I pF = 10−k, k = 3, 4, 5, 6
What NT is required to achieve the CV δ = 0.3 ?
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 13 / 26
Numerical Example
Linear Problem
I ∂F ⊂ Rd is a hyperplane
I d = 1000
I pF = 10−k, k = 3, 4, 5, 6
What NT is required to achieve the CV δ = 0.3 ?
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 13 / 26
Numerical Example
Linear Problem
I ∂F ⊂ Rd is a hyperplane
I d = 1000
I pF = 10−k, k = 3, 4, 5, 6
What NT is required to achieve the CV δ = 0.3 ?
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 13 / 26
MCMC Big Bang
Reliability methods based on MCMC:
Au and Beck (2001): Subset Simulation
Schueller et al (2004): Line Sampling
Ching et al (2005): Subset Simulation with Splitting
Ching et al (2005): Hybrid Subset Simulation
Katafygiotis and Cheung (2005): Two stage Subset Simulation
Katafygiotis et al (2007): Auxiliary Domain Method
Katafygiotis and Cheung (2007): Spherical Subset Simulation
Zuev and Katafygiotis (2007): Adaptive Linked Importance Sampling
Zuev and Katafygiotis (2011): Horseracing Simulation
Zuev et al (2011): Bayesian Subset Simulation
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 14 / 26
MCMC Big Bang
Reliability methods based on MCMC:
Au and Beck (2001): Subset Simulation
Schueller et al (2004): Line Sampling
Ching et al (2005): Subset Simulation with Splitting
Ching et al (2005): Hybrid Subset Simulation
Katafygiotis and Cheung (2005): Two stage Subset Simulation
Katafygiotis et al (2007): Auxiliary Domain Method
Katafygiotis and Cheung (2007): Spherical Subset Simulation
Zuev and Katafygiotis (2007): Adaptive Linked Importance Sampling
Zuev and Katafygiotis (2011): Horseracing Simulation
Zuev et al (2011): Bayesian Subset Simulation
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 14 / 26
Outline
1 Reliability Problem
2 Pre-MCMC era
3 First MCMC pancake
4 Subset Simulation
5 Enhancements for Subset Simulation
I Modified Metropolis-Hastings algorithm with Delayed Rejection
I Bayesian Subset Simulation
6 Summary
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 15 / 26
Modifications of the Metropolis-Hastings algorithm
MMH: Modified Metropolis-Hastings algorithm
I Au and Beck, 2001
MHDR: Metropolis-Hastings algorithm with delayed rejection
I Tierney and Mira, 1999
MMHDR: Modified Metropolis-Hastings algorithm with delayed rejection
I Zuev and Katafygiotis, 2011
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 16 / 26
Metropolis-Hastings algorithm with Delayed Rejection
Tierney and Mira (1999):
a1(xn, y1) = min
{1,π(y1)
π(xn)IF (y1)
}a2(xn, y1, y2) = min
{1,
π(y2)S1(y1|y2)(1− a1(y2, y1))
π(xn)S1(y1|xn)(1− a1(xn, y1))IF (y2)
}
Drawback: Inefficient in high dimensions
Reason: S1(·|xn) and S2(·|xn, y1) are d-dimensional PDFs
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 17 / 26
Metropolis-Hastings algorithm with Delayed Rejection
Tierney and Mira (1999):
a1(xn, y1) = min
{1,π(y1)
π(xn)IF (y1)
}a2(xn, y1, y2) = min
{1,
π(y2)S1(y1|y2)(1− a1(y2, y1))
π(xn)S1(y1|xn)(1− a1(xn, y1))IF (y2)
}
Drawback: Inefficient in high dimensions
Reason: S1(·|xn) and S2(·|xn, y1) are d-dimensional PDFs
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 17 / 26
Metropolis-Hastings algorithm with Delayed Rejection
Tierney and Mira (1999):
a1(xn, y1) = min
{1,π(y1)
π(xn)IF (y1)
}a2(xn, y1, y2) = min
{1,
π(y2)S1(y1|y2)(1− a1(y2, y1))
π(xn)S1(y1|xn)(1− a1(xn, y1))IF (y2)
}
Drawback: Inefficient in high dimensions
Reason: S1(·|xn) and S2(·|xn, y1) are d-dimensional PDFs
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 17 / 26
Metropolis-Hastings algorithm with Delayed Rejection
Tierney and Mira (1999):
a1(xn, y1) = min
{1,π(y1)
π(xn)IF (y1)
}a2(xn, y1, y2) = min
{1,
π(y2)S1(y1|y2)(1− a1(y2, y1))
π(xn)S1(y1|xn)(1− a1(xn, y1))IF (y2)
}
Drawback: Inefficient in high dimensions
Reason: S1(·|xn) and S2(·|xn, y1) are d-dimensional PDFs
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 17 / 26
Metropolis-Hastings algorithm with Delayed Rejection
Tierney and Mira (1999):
a1(xn, y1) = min
{1,π(y1)
π(xn)IF (y1)
}a2(xn, y1, y2) = min
{1,
π(y2)S1(y1|y2)(1− a1(y2, y1))
π(xn)S1(y1|xn)(1− a1(xn, y1))IF (y2)
}
Drawback: Inefficient in high dimensions
Reason: S1(·|xn) and S2(·|xn, y1) are d-dimensional PDFs
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 17 / 26
Modified MH algorithm with Delayed Rejection
Features of the Algorithm:
Samples generated by MMHDR are less correlated
then samples generated by MMH.
MMHDR needs more computational effort than MMH
for generating the same number of samples.
Whether MMHDR is useful for reliability problems depends on whether the
gained reduction in variance compensates for the additional cost.
With fixed computational effort:
I MMH: more Markov chains with more correlated states
I MMHDR: fewer Markov chains with less correlated states
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 18 / 26
Modified MH algorithm with Delayed Rejection
Features of the Algorithm:
Samples generated by MMHDR are less correlated
then samples generated by MMH.
MMHDR needs more computational effort than MMH
for generating the same number of samples.
Whether MMHDR is useful for reliability problems depends on whether the
gained reduction in variance compensates for the additional cost.
With fixed computational effort:
I MMH: more Markov chains with more correlated states
I MMHDR: fewer Markov chains with less correlated states
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 18 / 26
Modified MH algorithm with Delayed Rejection
Features of the Algorithm:
Samples generated by MMHDR are less correlated
then samples generated by MMH.
MMHDR needs more computational effort than MMH
for generating the same number of samples.
Whether MMHDR is useful for reliability problems depends on whether the
gained reduction in variance compensates for the additional cost.
With fixed computational effort:
I MMH: more Markov chains with more correlated states
I MMHDR: fewer Markov chains with less correlated states
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 18 / 26
Modified MH algorithm with Delayed Rejection
Features of the Algorithm:
Samples generated by MMHDR are less correlated
then samples generated by MMH.
MMHDR needs more computational effort than MMH
for generating the same number of samples.
Whether MMHDR is useful for reliability problems depends on whether the
gained reduction in variance compensates for the additional cost.
With fixed computational effort:
I MMH: more Markov chains with more correlated states
I MMHDR: fewer Markov chains with less correlated states
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 18 / 26
Modified MH algorithm with Delayed Rejection
Features of the Algorithm:
Samples generated by MMHDR are less correlated
then samples generated by MMH.
MMHDR needs more computational effort than MMH
for generating the same number of samples.
Whether MMHDR is useful for reliability problems depends on whether the
gained reduction in variance compensates for the additional cost.
With fixed computational effort:
I MMH: more Markov chains with more correlated states
I MMHDR: fewer Markov chains with less correlated states
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 18 / 26
Modified MH algorithm with Delayed Rejection
Features of the Algorithm:
Samples generated by MMHDR are less correlated
then samples generated by MMH.
MMHDR needs more computational effort than MMH
for generating the same number of samples.
Whether MMHDR is useful for reliability problems depends on whether the
gained reduction in variance compensates for the additional cost.
With fixed computational effort:
I MMH: more Markov chains with more correlated states
I MMHDR: fewer Markov chains with less correlated states
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 18 / 26
Numerical ExampleLinear problem
Geometry
I d = 1000
I pF = 10−5, β = 4.265
Proposal PDFs
I MMH: Sj(·|xj0) = N (xj0, 1)
I MMHDR: Sj1,2(·|x
j0) = N (xj0, 1)
Subset Simulation
MMH(1): SS + MMH, N = 103
MMHDR(1.4): SS + MMHDR, N = 103
MMH(1.4): SS + MMH, N = 1450
Reduction in CV is 11%
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 19 / 26
Numerical ExampleLinear problem
Geometry
I d = 1000
I pF = 10−5, β = 4.265
Proposal PDFs
I MMH: Sj(·|xj0) = N (xj0, 1)
I MMHDR: Sj1,2(·|x
j0) = N (xj0, 1)
Subset Simulation
MMH(1): SS + MMH, N = 103
MMHDR(1.4): SS + MMHDR, N = 103
MMH(1.4): SS + MMH, N = 1450
Reduction in CV is 11%
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 19 / 26
Numerical ExampleLinear problem
Geometry
I d = 1000
I pF = 10−5, β = 4.265
Proposal PDFs
I MMH: Sj(·|xj0) = N (xj0, 1)
I MMHDR: Sj1,2(·|x
j0) = N (xj0, 1)
Subset Simulation
MMH(1): SS + MMH, N = 103
MMHDR(1.4): SS + MMHDR, N = 103
MMH(1.4): SS + MMH, N = 1450
Reduction in CV is 11%
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 19 / 26
Numerical ExampleLinear problem
Geometry
I d = 1000
I pF = 10−5, β = 4.265
Proposal PDFs
I MMH: Sj(·|xj0) = N (xj0, 1)
I MMHDR: Sj1,2(·|x
j0) = N (xj0, 1)
Subset Simulation
MMH(1): SS + MMH, N = 103
MMHDR(1.4): SS + MMHDR, N = 103
MMH(1.4): SS + MMH, N = 1450
Reduction in CV is 11%
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 19 / 26
Numerical ExampleLinear problem
Geometry
I d = 1000
I pF = 10−5, β = 4.265
Proposal PDFs
I MMH: Sj(·|xj0) = N (xj0, 1)
I MMHDR: Sj1,2(·|x
j0) = N (xj0, 1)
Subset Simulation
MMH(1): SS + MMH, N = 103
MMHDR(1.4): SS + MMHDR, N = 103
MMH(1.4): SS + MMH, N = 1450
Reduction in CV is 11%
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 19 / 26
“Bayesianization” of Subset Simulation
The key idea of SS:
pF =
m∏k=1
pk, pk = P (Fk|Fk−1)
Original (“frequentist”) SS:
pk ≈ pk =1
N
N∑i=1
IFk(x
(i)k−1) =
nkN, pF ≈ pF =
m∏k=1
nkN
Bayesian SS:
1 Specify prior PDFs f(pk) for all pk = P (Fk|Fk−1), k = 1, . . . ,m.
2 Find the posterior PDFs f(pk|Dk−1) via Bayes’ theorem,
using new data Dk−1 = {x(1)k−1, . . . , x(N)k−1 ∼ π(·|Fk−1)}
3 Obtain the posterior PDF f(pF | ∪m−1k=0 Dk) of pF =∏mk=1 pk
from f(p1|D0), . . . , f(pm|Dm−1).
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 20 / 26
“Bayesianization” of Subset Simulation
The key idea of SS:
pF =
m∏k=1
pk, pk = P (Fk|Fk−1)
Original (“frequentist”) SS:
pk ≈ pk =1
N
N∑i=1
IFk(x
(i)k−1) =
nkN, pF ≈ pF =
m∏k=1
nkN
Bayesian SS:
1 Specify prior PDFs f(pk) for all pk = P (Fk|Fk−1), k = 1, . . . ,m.
2 Find the posterior PDFs f(pk|Dk−1) via Bayes’ theorem,
using new data Dk−1 = {x(1)k−1, . . . , x(N)k−1 ∼ π(·|Fk−1)}
3 Obtain the posterior PDF f(pF | ∪m−1k=0 Dk) of pF =∏mk=1 pk
from f(p1|D0), . . . , f(pm|Dm−1).
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 20 / 26
“Bayesianization” of Subset Simulation
The key idea of SS:
pF =
m∏k=1
pk, pk = P (Fk|Fk−1)
Original (“frequentist”) SS:
pk ≈ pk =1
N
N∑i=1
IFk(x
(i)k−1) =
nkN, pF ≈ pF =
m∏k=1
nkN
Bayesian SS:
1 Specify prior PDFs f(pk) for all pk = P (Fk|Fk−1), k = 1, . . . ,m.
2 Find the posterior PDFs f(pk|Dk−1) via Bayes’ theorem,
using new data Dk−1 = {x(1)k−1, . . . , x(N)k−1 ∼ π(·|Fk−1)}
3 Obtain the posterior PDF f(pF | ∪m−1k=0 Dk) of pF =∏mk=1 pk
from f(p1|D0), . . . , f(pm|Dm−1).
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 20 / 26
“Bayesianization” of Subset Simulation
The key idea of SS:
pF =
m∏k=1
pk, pk = P (Fk|Fk−1)
Original (“frequentist”) SS:
pk ≈ pk =1
N
N∑i=1
IFk(x
(i)k−1) =
nkN, pF ≈ pF =
m∏k=1
nkN
Bayesian SS:
1 Specify prior PDFs f(pk) for all pk = P (Fk|Fk−1), k = 1, . . . ,m.
2 Find the posterior PDFs f(pk|Dk−1) via Bayes’ theorem,
using new data Dk−1 = {x(1)k−1, . . . , x(N)k−1 ∼ π(·|Fk−1)}
3 Obtain the posterior PDF f(pF | ∪m−1k=0 Dk) of pF =∏mk=1 pk
from f(p1|D0), . . . , f(pm|Dm−1).
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 20 / 26
“Bayesianization” of Subset Simulation
The key idea of SS:
pF =
m∏k=1
pk, pk = P (Fk|Fk−1)
Original (“frequentist”) SS:
pk ≈ pk =1
N
N∑i=1
IFk(x
(i)k−1) =
nkN, pF ≈ pF =
m∏k=1
nkN
Bayesian SS:
1 Specify prior PDFs f(pk) for all pk = P (Fk|Fk−1), k = 1, . . . ,m.
2 Find the posterior PDFs f(pk|Dk−1) via Bayes’ theorem,
using new data Dk−1 = {x(1)k−1, . . . , x(N)k−1 ∼ π(·|Fk−1)}
3 Obtain the posterior PDF f(pF | ∪m−1k=0 Dk) of pF =∏mk=1 pk
from f(p1|D0), . . . , f(pm|Dm−1).
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 20 / 26
“Bayesianization” of Subset Simulation
The key idea of SS:
pF =
m∏k=1
pk, pk = P (Fk|Fk−1)
Original (“frequentist”) SS:
pk ≈ pk =1
N
N∑i=1
IFk(x
(i)k−1) =
nkN, pF ≈ pF =
m∏k=1
nkN
Bayesian SS:
1 Specify prior PDFs f(pk) for all pk = P (Fk|Fk−1), k = 1, . . . ,m.
2 Find the posterior PDFs f(pk|Dk−1) via Bayes’ theorem,
using new data Dk−1 = {x(1)k−1, . . . , x(N)k−1 ∼ π(·|Fk−1)}
3 Obtain the posterior PDF f(pF | ∪m−1k=0 Dk) of pF =∏mk=1 pk
from f(p1|D0), . . . , f(pm|Dm−1).
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 20 / 26
Prior and Posterior for pk = P (Fk|Fk−1)
1 Prior PDF f(pk)
Principle of Maximum Entropy:
f(pk) = 1, 0 ≤ pk ≤ 1.
2 Posterior PDF f(pk|Dk−1)
I If x(1)k−1, . . . , x
(N)k−1 are i.i.d. according to π(·|Fk−1)
⇒ IFk (x(1)k−1), . . . , IFk (x
(N)k−1) can be interpreted as Bernoulli trials
⇒ Bayes’ Theorem (1763):
f(pk|Dk−1) =pnkk (1− pk)N−nk
B(nk + 1, N − nk + 1)
I In fact, x(1)k−1, . . . , x
(N)k−1 are MCMC samples (for k ≥ 2)
⇒ x(1)k−1, . . . , x
(N)k−1 ∼ π(·|Fk−1), however, they are not independent
f(pk|Dk−1) ≈pnkk (1− pk)N−nk
B(nk + 1, N − nk + 1)
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 21 / 26
Prior and Posterior for pk = P (Fk|Fk−1)
1 Prior PDF f(pk)
Principle of Maximum Entropy:
f(pk) = 1, 0 ≤ pk ≤ 1.
2 Posterior PDF f(pk|Dk−1)
I If x(1)k−1, . . . , x
(N)k−1 are i.i.d. according to π(·|Fk−1)
⇒ IFk (x(1)k−1), . . . , IFk (x
(N)k−1) can be interpreted as Bernoulli trials
⇒ Bayes’ Theorem (1763):
f(pk|Dk−1) =pnkk (1− pk)N−nk
B(nk + 1, N − nk + 1)
I In fact, x(1)k−1, . . . , x
(N)k−1 are MCMC samples (for k ≥ 2)
⇒ x(1)k−1, . . . , x
(N)k−1 ∼ π(·|Fk−1), however, they are not independent
f(pk|Dk−1) ≈pnkk (1− pk)N−nk
B(nk + 1, N − nk + 1)
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 21 / 26
Prior and Posterior for pk = P (Fk|Fk−1)
1 Prior PDF f(pk)
Principle of Maximum Entropy:
f(pk) = 1, 0 ≤ pk ≤ 1.
2 Posterior PDF f(pk|Dk−1)
I If x(1)k−1, . . . , x
(N)k−1 are i.i.d. according to π(·|Fk−1)
⇒ IFk (x(1)k−1), . . . , IFk (x
(N)k−1) can be interpreted as Bernoulli trials
⇒ Bayes’ Theorem (1763):
f(pk|Dk−1) =pnkk (1− pk)N−nk
B(nk + 1, N − nk + 1)
I In fact, x(1)k−1, . . . , x
(N)k−1 are MCMC samples (for k ≥ 2)
⇒ x(1)k−1, . . . , x
(N)k−1 ∼ π(·|Fk−1), however, they are not independent
f(pk|Dk−1) ≈pnkk (1− pk)N−nk
B(nk + 1, N − nk + 1)
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 21 / 26
Prior and Posterior for pk = P (Fk|Fk−1)
1 Prior PDF f(pk)
Principle of Maximum Entropy:
f(pk) = 1, 0 ≤ pk ≤ 1.
2 Posterior PDF f(pk|Dk−1)
I If x(1)k−1, . . . , x
(N)k−1 are i.i.d. according to π(·|Fk−1)
⇒ IFk (x(1)k−1), . . . , IFk (x
(N)k−1) can be interpreted as Bernoulli trials
⇒ Bayes’ Theorem (1763):
f(pk|Dk−1) =pnkk (1− pk)N−nk
B(nk + 1, N − nk + 1)
I In fact, x(1)k−1, . . . , x
(N)k−1 are MCMC samples (for k ≥ 2)
⇒ x(1)k−1, . . . , x
(N)k−1 ∼ π(·|Fk−1), however, they are not independent
f(pk|Dk−1) ≈pnkk (1− pk)N−nk
B(nk + 1, N − nk + 1)
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 21 / 26
Prior and Posterior for pk = P (Fk|Fk−1)
1 Prior PDF f(pk)
Principle of Maximum Entropy:
f(pk) = 1, 0 ≤ pk ≤ 1.
2 Posterior PDF f(pk|Dk−1)
I If x(1)k−1, . . . , x
(N)k−1 are i.i.d. according to π(·|Fk−1)
⇒ IFk (x(1)k−1), . . . , IFk (x
(N)k−1) can be interpreted as Bernoulli trials
⇒ Bayes’ Theorem (1763):
f(pk|Dk−1) =pnkk (1− pk)N−nk
B(nk + 1, N − nk + 1)
I In fact, x(1)k−1, . . . , x
(N)k−1 are MCMC samples (for k ≥ 2)
⇒ x(1)k−1, . . . , x
(N)k−1 ∼ π(·|Fk−1), however, they are not independent
f(pk|Dk−1) ≈pnkk (1− pk)N−nk
B(nk + 1, N − nk + 1)
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 21 / 26
Posterior PDF for pF
Last step: To find the PDF of pF =∏mk=1 pk, given the PDFs of all factors
pk ∼ Be(nk + 1, N − nk + 1)
Idea: To approximate pF by a single beta variable
Theorem (Da-Yin Fan, 1991)
Let X1, . . . , Xm be beta variables, Xk ∼ Beta(ak, bk), and Y = X1X2 . . . Xm.
Then Y is approximately distributed as Y ∼ Beta(a, b), where
a = µ1µ1 − µ2
µ2 − µ21
, b = (1− µ1)µ1 − µ2
µ2 − µ21
,
µ1 =
m∏k=1
akak + bk
, µ2 =
m∏k=1
ak(ak + 1)
(ak + bk)(ak + bk + 1).
Nice property of this approximation: E[Y ] = E[Y ], E[Y 2] = E[Y 2]
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 22 / 26
Posterior PDF for pF
Last step: To find the PDF of pF =∏mk=1 pk, given the PDFs of all factors
pk ∼ Be(nk + 1, N − nk + 1)
Idea: To approximate pF by a single beta variable
Theorem (Da-Yin Fan, 1991)
Let X1, . . . , Xm be beta variables, Xk ∼ Beta(ak, bk), and Y = X1X2 . . . Xm.
Then Y is approximately distributed as Y ∼ Beta(a, b), where
a = µ1µ1 − µ2
µ2 − µ21
, b = (1− µ1)µ1 − µ2
µ2 − µ21
,
µ1 =
m∏k=1
akak + bk
, µ2 =
m∏k=1
ak(ak + 1)
(ak + bk)(ak + bk + 1).
Nice property of this approximation: E[Y ] = E[Y ], E[Y 2] = E[Y 2]
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 22 / 26
Posterior PDF for pF
Last step: To find the PDF of pF =∏mk=1 pk, given the PDFs of all factors
pk ∼ Be(nk + 1, N − nk + 1)
Idea: To approximate pF by a single beta variable
Theorem (Da-Yin Fan, 1991)
Let X1, . . . , Xm be beta variables, Xk ∼ Beta(ak, bk), and Y = X1X2 . . . Xm.
Then Y is approximately distributed as Y ∼ Beta(a, b), where
a = µ1µ1 − µ2
µ2 − µ21
, b = (1− µ1)µ1 − µ2
µ2 − µ21
,
µ1 =
m∏k=1
akak + bk
, µ2 =
m∏k=1
ak(ak + 1)
(ak + bk)(ak + bk + 1).
Nice property of this approximation: E[Y ] = E[Y ], E[Y 2] = E[Y 2]
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 22 / 26
Posterior PDF for pF
Last step: To find the PDF of pF =∏mk=1 pk, given the PDFs of all factors
pk ∼ Be(nk + 1, N − nk + 1)
Idea: To approximate pF by a single beta variable
Theorem (Da-Yin Fan, 1991)
Let X1, . . . , Xm be beta variables, Xk ∼ Beta(ak, bk), and Y = X1X2 . . . Xm.
Then Y is approximately distributed as Y ∼ Beta(a, b), where
a = µ1µ1 − µ2
µ2 − µ21
, b = (1− µ1)µ1 − µ2
µ2 − µ21
,
µ1 =
m∏k=1
akak + bk
, µ2 =
m∏k=1
ak(ak + 1)
(ak + bk)(ak + bk + 1).
Nice property of this approximation: E[Y ] = E[Y ], E[Y 2] = E[Y 2]
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 22 / 26
Bayesian Subset Simulation
Point estimate pF PDF f(pF ) = Be(pF |a, b)
a =
∏mk=1
nk+1N+2
(1−
∏mk=1
nk+2N+3
)∏mk=1
nk+2N+3 −
∏mk=1
nk+1N+2
b =
(1−
∏mk=1
nk+1N+2
)(1−
∏mk=1
nk+2N+3
)∏mk=1
nk+2N+3 −
∏mk=1
nk+1N+2
What is the relationship between f(pF ) and pF ?
limN→∞
Ef [pF ] = limN→∞
pF = pF
Why is Bayesian Subset Simulation useful?
I CV of f(pF ) can be considered as a measure of uncertainty in the value of pFI The PDF f(pF ) can be fully used for life-cost analyses, decision making, etc.
E[Loss(pF )] =
∫Loss(pF )f(pF )dpF
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 23 / 26
Bayesian Subset Simulation
Point estimate pF PDF f(pF ) = Be(pF |a, b)
a =
∏mk=1
nk+1N+2
(1−
∏mk=1
nk+2N+3
)∏mk=1
nk+2N+3 −
∏mk=1
nk+1N+2
b =
(1−
∏mk=1
nk+1N+2
)(1−
∏mk=1
nk+2N+3
)∏mk=1
nk+2N+3 −
∏mk=1
nk+1N+2
What is the relationship between f(pF ) and pF ?
limN→∞
Ef [pF ] = limN→∞
pF = pF
Why is Bayesian Subset Simulation useful?
I CV of f(pF ) can be considered as a measure of uncertainty in the value of pFI The PDF f(pF ) can be fully used for life-cost analyses, decision making, etc.
E[Loss(pF )] =
∫Loss(pF )f(pF )dpF
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 23 / 26
Bayesian Subset Simulation
Point estimate pF PDF f(pF ) = Be(pF |a, b)
a =
∏mk=1
nk+1N+2
(1−
∏mk=1
nk+2N+3
)∏mk=1
nk+2N+3 −
∏mk=1
nk+1N+2
b =
(1−
∏mk=1
nk+1N+2
)(1−
∏mk=1
nk+2N+3
)∏mk=1
nk+2N+3 −
∏mk=1
nk+1N+2
What is the relationship between f(pF ) and pF ?
limN→∞
Ef [pF ] = limN→∞
pF = pF
Why is Bayesian Subset Simulation useful?
I CV of f(pF ) can be considered as a measure of uncertainty in the value of pFI The PDF f(pF ) can be fully used for life-cost analyses, decision making, etc.
E[Loss(pF )] =
∫Loss(pF )f(pF )dpF
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 23 / 26
Bayesian Subset Simulation
Point estimate pF PDF f(pF ) = Be(pF |a, b)
a =
∏mk=1
nk+1N+2
(1−
∏mk=1
nk+2N+3
)∏mk=1
nk+2N+3 −
∏mk=1
nk+1N+2
b =
(1−
∏mk=1
nk+1N+2
)(1−
∏mk=1
nk+2N+3
)∏mk=1
nk+2N+3 −
∏mk=1
nk+1N+2
What is the relationship between f(pF ) and pF ?
limN→∞
Ef [pF ] = limN→∞
pF = pF
Why is Bayesian Subset Simulation useful?
I CV of f(pF ) can be considered as a measure of uncertainty in the value of pFI The PDF f(pF ) can be fully used for life-cost analyses, decision making, etc.
E[Loss(pF )] =
∫Loss(pF )f(pF )dpF
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 23 / 26
Bayesian Subset Simulation
Point estimate pF PDF f(pF ) = Be(pF |a, b)
a =
∏mk=1
nk+1N+2
(1−
∏mk=1
nk+2N+3
)∏mk=1
nk+2N+3 −
∏mk=1
nk+1N+2
b =
(1−
∏mk=1
nk+1N+2
)(1−
∏mk=1
nk+2N+3
)∏mk=1
nk+2N+3 −
∏mk=1
nk+1N+2
What is the relationship between f(pF ) and pF ?
limN→∞
Ef [pF ] = limN→∞
pF = pF
Why is Bayesian Subset Simulation useful?
I CV of f(pF ) can be considered as a measure of uncertainty in the value of pFI The PDF f(pF ) can be fully used for life-cost analyses, decision making, etc.
E[Loss(pF )] =
∫Loss(pF )f(pF )dpF
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 23 / 26
Bayesian Subset Simulation
Point estimate pF PDF f(pF ) = Be(pF |a, b)
a =
∏mk=1
nk+1N+2
(1−
∏mk=1
nk+2N+3
)∏mk=1
nk+2N+3 −
∏mk=1
nk+1N+2
b =
(1−
∏mk=1
nk+1N+2
)(1−
∏mk=1
nk+2N+3
)∏mk=1
nk+2N+3 −
∏mk=1
nk+1N+2
What is the relationship between f(pF ) and pF ?
limN→∞
Ef [pF ] = limN→∞
pF = pF
Why is Bayesian Subset Simulation useful?
I CV of f(pF ) can be considered as a measure of uncertainty in the value of pF
I The PDF f(pF ) can be fully used for life-cost analyses, decision making, etc.
E[Loss(pF )] =
∫Loss(pF )f(pF )dpF
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 23 / 26
Bayesian Subset Simulation
Point estimate pF PDF f(pF ) = Be(pF |a, b)
a =
∏mk=1
nk+1N+2
(1−
∏mk=1
nk+2N+3
)∏mk=1
nk+2N+3 −
∏mk=1
nk+1N+2
b =
(1−
∏mk=1
nk+1N+2
)(1−
∏mk=1
nk+2N+3
)∏mk=1
nk+2N+3 −
∏mk=1
nk+1N+2
What is the relationship between f(pF ) and pF ?
limN→∞
Ef [pF ] = limN→∞
pF = pF
Why is Bayesian Subset Simulation useful?
I CV of f(pF ) can be considered as a measure of uncertainty in the value of pFI The PDF f(pF ) can be fully used for life-cost analyses, decision making, etc.
E[Loss(pF )] =
∫Loss(pF )f(pF )dpF
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 23 / 26
Elasto-Plastic Structure Subjected to Ground Motion
S.K. Au (Computers & Structures, 2005):
2D moment-resisting steel frame
Synthetic ground motion a = a(Z)
I Z = (Z1, . . . , Zd)i.i.d∼ N (0, 1)
IZ−→ Filter
a(Z)−−−→I d = 1001
Failure domain:
F = {Z ∈ Rd : δmax(Z) > b}
δmax = maxi=1,...,6
δi
δi is the maximum absolute
interstory drift ratio of the ith story
within the duration of study, 30 s
b = 0.5%⇒ pF ≈ 8.9× 10−3
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 24 / 26
Elasto-Plastic Structure Subjected to Ground Motion
S.K. Au (Computers & Structures, 2005):
2D moment-resisting steel frame
Synthetic ground motion a = a(Z)
I Z = (Z1, . . . , Zd)i.i.d∼ N (0, 1)
IZ−→ Filter
a(Z)−−−→I d = 1001
Failure domain:
F = {Z ∈ Rd : δmax(Z) > b}
δmax = maxi=1,...,6
δi
δi is the maximum absolute
interstory drift ratio of the ith story
within the duration of study, 30 s
b = 0.5%⇒ pF ≈ 8.9× 10−3
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 24 / 26
Summary
MCMC is very useful for solving engineering problems
I Reliability Engineering
Subset Simulation (Au and Beck, 2001)
I a very efficient MCMC method for estimation of small failure probabilities
Enhancements for Subset Simulation
I MMHDR = MMH (Au and Beck, 2001) + MHDR (Tierney and Mira, 1999)
I Bayesian Subset Simulation
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 25 / 26
Summary
MCMC is very useful for solving engineering problems
I Reliability Engineering
Subset Simulation (Au and Beck, 2001)
I a very efficient MCMC method for estimation of small failure probabilities
Enhancements for Subset Simulation
I MMHDR = MMH (Au and Beck, 2001) + MHDR (Tierney and Mira, 1999)
I Bayesian Subset Simulation
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 25 / 26
Summary
MCMC is very useful for solving engineering problems
I Reliability Engineering
Subset Simulation (Au and Beck, 2001)
I a very efficient MCMC method for estimation of small failure probabilities
Enhancements for Subset Simulation
I MMHDR = MMH (Au and Beck, 2001) + MHDR (Tierney and Mira, 1999)
I Bayesian Subset Simulation
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 25 / 26
Summary
MCMC is very useful for solving engineering problems
I Reliability Engineering
Subset Simulation (Au and Beck, 2001)
I a very efficient MCMC method for estimation of small failure probabilities
Enhancements for Subset Simulation
I MMHDR = MMH (Au and Beck, 2001) + MHDR (Tierney and Mira, 1999)
I Bayesian Subset Simulation
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 25 / 26
Thank you for attention!
Konstantin Zuev (USC) MCMC Revolution in Reliability Engineering SCPS 2011 26 / 26