algorithms for uncertainty quantification...repetition from previous lecture sampling methods!a...

Algorithms for Uncertainty QuantificationTobias Neckel, Ionut,-Gabriel Farcas,

Lehrstuhl Informatik V

Summer Semester 2017

Lecture 4: More advanced samplingtechniques

Repetition from previous lecture

Repetition from previous lecture• Sampling methods→ a popular technique for uncertainty propagation

• Most widely used sampling approach→ Monte Carlo sampling

• Monte Carlo sampling→ simple, robust, independent of probability distribution, number of randomparameters, ...

• ... but slow convergence rate

• Model problem→ damped linear oscillator

• Uncertainty in some input parameters→ higher impact in the output uncertainty

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 4

Monte Carlo sampling error analysis

Short error analysis for standard Monte CarloRemember

• for N samples, the MCS error is O( 1√N)

How did we get that?

• Monte Carlo sampling↔ averaging

• let f : [0,1]→ R and I :=∫ 1

0 f (x)dx

• I ≈ If = 1N ∑

Ni=1 f (Ui), Ui ∼U (0,1)

• if σ2f = Var[f (x)], E[(I− If )2] = σ2

f /N

• σ2f ≈ σ2

f = 1N−1 ∑

Ni=1(f (Ui)− If )2

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 6

Short error analysis for standard Monte CarloRemember

• for N samples, the MCS error is O( 1√N)

How did we get that?

• Monte Carlo sampling↔ averaging

• let f : [0,1]→ R and I :=∫ 1

0 f (x)dx

• I ≈ If = 1N ∑

Ni=1 f (Ui), Ui ∼U (0,1)

• if σ2f = Var[f (x)], E[(I− If )2] = σ2

f /N

• σ2f ≈ σ2

f = 1N−1 ∑

Ni=1(f (Ui)− If )2

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 6

Short error analysis for standard Monte CarloRemember

• for N samples, the MCS error is O( 1√N)

How did we get that?

• Monte Carlo sampling↔ averaging

• let f : [0,1]→ R and I :=∫ 1

0 f (x)dx

• I ≈ If = 1N ∑

Ni=1 f (Ui), Ui ∼U (0,1)

• if σ2f = Var[f (x)], E[(I− If )2] = σ2

f /N

• σ2f ≈ σ2

f = 1N−1 ∑

Ni=1(f (Ui)− If )2

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 6

Short error analysis for standard Monte CarloRemember

• for N samples, the MCS error is O( 1√N)

How did we get that?

• Monte Carlo sampling↔ averaging

• let f : [0,1]→ R and I :=∫ 1

0 f (x)dx

• I ≈ If = 1N ∑

Ni=1 f (Ui), Ui ∼U (0,1)

• if σ2f = Var[f (x)], E[(I− If )2] = σ2

f /N

• σ2f ≈ σ2

f = 1N−1 ∑

Ni=1(f (Ui)− If )2

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 6

Short error analysis for standard Monte CarloRemember

• for N samples, the MCS error is O( 1√N)

How did we get that?

• Monte Carlo sampling↔ averaging

• let f : [0,1]→ R and I :=∫ 1

0 f (x)dx

• I ≈ If = 1N ∑

Ni=1 f (Ui), Ui ∼U (0,1)

• if σ2f = Var[f (x)], E[(I− If )2] = σ2

f /N

• σ2f ≈ σ2

f = 1N−1 ∑

Ni=1(f (Ui)− If )2

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 6

Short error analysis for standard Monte CarloRemember

• for N samples, the MCS error is O( 1√N)

How did we get that?

• Monte Carlo sampling↔ averaging

• let f : [0,1]→ R and I :=∫ 1

0 f (x)dx

• I ≈ If = 1N ∑

Ni=1 f (Ui), Ui ∼U (0,1)

• if σ2f = Var[f (x)], E[(I− If )2] = σ2

f /N

• σ2f ≈ σ2

f = 1N−1 ∑

Ni=1(f (Ui)− If )2

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 6

Improving standard Monte Carlosampling

Towards more advanced sampling techniques• How to increase the accuracy of standard Monte Carlo? (E[(I− If )2] = σ2

f /N)

− improve your code• parallelize• vectorize• remove if statements• use memory efficiently• etc.

− increase N• not desirable

− decrease σ2f

• desirable

− improve the random number generation

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 8

Towards more advanced sampling techniques• How to increase the accuracy of standard Monte Carlo? (E[(I− If )2] = σ2

f /N)− improve your code• parallelize• vectorize• remove if statements• use memory efficiently• etc.

− increase N• not desirable

− decrease σ2f

• desirable

− improve the random number generation

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 8

Towards more advanced sampling techniques• How to increase the accuracy of standard Monte Carlo? (E[(I− If )2] = σ2

f /N)− improve your code• parallelize• vectorize• remove if statements• use memory efficiently• etc.

− increase N• not desirable

− decrease σ2f

• desirable

− improve the random number generation

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 8

Towards more advanced sampling techniques• How to increase the accuracy of standard Monte Carlo? (E[(I− If )2] = σ2

f /N)− improve your code• parallelize• vectorize• remove if statements• use memory efficiently• etc.

− increase N• not desirable

− decrease σ2f

• desirable

− improve the random number generation

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 8

Towards more advanced sampling techniques• How to increase the accuracy of standard Monte Carlo? (E[(I− If )2] = σ2

f /N)− improve your code• parallelize• vectorize• remove if statements• use memory efficiently• etc.

− increase N• not desirable

− decrease σ2f

• desirable

− improve the random number generation

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 8

Improving standard Monte Carlo sampling

Variance-minimization techniques

• antithetic sampling

• importance sampling

• stratified sampling

• control variates

Alternative random number generation techniques

• Fibonacci generators

• latin hypercube sampling

• Sobol’ sequences

• Halton sequences

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 9

Improving standard Monte Carlo samplingVariance-minimization techniques

• antithetic sampling

• importance sampling

• stratified sampling

• control variates

Alternative random number generation techniques

• Fibonacci generators

• latin hypercube sampling

• Sobol’ sequences

• Halton sequences

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 9

Improving standard Monte Carlo samplingVariance-minimization techniques

• antithetic sampling

• importance sampling

• stratified sampling

• control variates

Alternative random number generation techniques

• Fibonacci generators

• latin hypercube sampling

• Sobol’ sequences

• Halton sequences

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 9

Improving standard Monte Carlo samplingVariance-minimization techniques

• antithetic sampling

• importance sampling

• stratified sampling

• control variates

Alternative random number generation techniques

• Fibonacci generators

• latin hypercube sampling

• Sobol’ sequences

• Halton sequences

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 9

Improving standard Monte Carlo samplingVariance-minimization techniques

• antithetic sampling

• importance sampling

• stratified sampling

• control variates

Alternative random number generation techniques

• Fibonacci generators

• latin hypercube sampling

• Sobol’ sequences

• Halton sequences

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 9

Improving standard Monte Carlo samplingVariance-minimization techniques

• antithetic sampling

• importance sampling

• stratified sampling

• control variates

Alternative random number generation techniques

• Fibonacci generators

• latin hypercube sampling

• Sobol’ sequences

• Halton sequences

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 9

Improving standard Monte Carlo samplingVariance-minimization techniques

• antithetic sampling

• importance sampling

• stratified sampling

• control variates

Alternative random number generation techniques

• Fibonacci generators

• latin hypercube sampling

• Sobol’ sequences

• Halton sequences

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 9

Improving standard Monte Carlo samplingVariance-minimization techniques

• antithetic sampling

• importance sampling

• stratified sampling

• control variates

Alternative random number generation techniques

• Fibonacci generators

• latin hypercube sampling

• Sobol’ sequences

• Halton sequences

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 9

Improving standard Monte Carlo samplingVariance-minimization techniques

• antithetic sampling

• importance sampling

• stratified sampling

• control variates

Alternative random number generation techniques

• Fibonacci generators

• latin hypercube sampling

• Sobol’ sequences

• Halton sequences

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 9

Improving standard Monte Carlo samplingVariance-minimization techniques

• antithetic sampling

• importance sampling

• stratified sampling

• control variates

Alternative random number generation techniques

• Fibonacci generators

• latin hypercube sampling

• Sobol’ sequences

• Halton sequences

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 9

Variance reduction techniques

Antithetic sampling

• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R• assume fX (x) is symmetric, c being the center of symmetry

• let t(X1, . . . ,Xn) denote an estimator

• let x ∈ supp(X ); the symmetric of x w.r.t. c is x = 2c−x

• symmetry implies fX (x) = fX (x)

• sample n/2 samples X1, . . . ,Xn/2 from ∼ fX (x)

• the remaining n/2 samples X1, . . . , Xn/2 are obtained via reflection

• then t(X1, . . . ,Xn) = t(X1, . . . ,Xn/2)+ t(X1, . . . , Xn/2)

• example: if U ∼U (0,1), take U ∼U (0,1); the antithetic samples are, U = 1−U

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 11

Antithetic sampling• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R

• assume fX (x) is symmetric, c being the center of symmetry

• let t(X1, . . . ,Xn) denote an estimator

• let x ∈ supp(X ); the symmetric of x w.r.t. c is x = 2c−x

• symmetry implies fX (x) = fX (x)

• sample n/2 samples X1, . . . ,Xn/2 from ∼ fX (x)

• the remaining n/2 samples X1, . . . , Xn/2 are obtained via reflection

• then t(X1, . . . ,Xn) = t(X1, . . . ,Xn/2)+ t(X1, . . . , Xn/2)

• example: if U ∼U (0,1), take U ∼U (0,1); the antithetic samples are, U = 1−U

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 11

Antithetic sampling• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R• assume fX (x) is symmetric, c being the center of symmetry

• let t(X1, . . . ,Xn) denote an estimator

• let x ∈ supp(X ); the symmetric of x w.r.t. c is x = 2c−x

• symmetry implies fX (x) = fX (x)

• sample n/2 samples X1, . . . ,Xn/2 from ∼ fX (x)

• the remaining n/2 samples X1, . . . , Xn/2 are obtained via reflection

• then t(X1, . . . ,Xn) = t(X1, . . . ,Xn/2)+ t(X1, . . . , Xn/2)

• example: if U ∼U (0,1), take U ∼U (0,1); the antithetic samples are, U = 1−U

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 11

Antithetic sampling• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R• assume fX (x) is symmetric, c being the center of symmetry

• let t(X1, . . . ,Xn) denote an estimator

• let x ∈ supp(X ); the symmetric of x w.r.t. c is x = 2c−x

• symmetry implies fX (x) = fX (x)

• sample n/2 samples X1, . . . ,Xn/2 from ∼ fX (x)

• the remaining n/2 samples X1, . . . , Xn/2 are obtained via reflection

• then t(X1, . . . ,Xn) = t(X1, . . . ,Xn/2)+ t(X1, . . . , Xn/2)

• example: if U ∼U (0,1), take U ∼U (0,1); the antithetic samples are, U = 1−U

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 11

Antithetic sampling• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R• assume fX (x) is symmetric, c being the center of symmetry

• let t(X1, . . . ,Xn) denote an estimator

• let x ∈ supp(X ); the symmetric of x w.r.t. c is x = 2c−x

• symmetry implies fX (x) = fX (x)

• sample n/2 samples X1, . . . ,Xn/2 from ∼ fX (x)

• the remaining n/2 samples X1, . . . , Xn/2 are obtained via reflection

• then t(X1, . . . ,Xn) = t(X1, . . . ,Xn/2)+ t(X1, . . . , Xn/2)

• example: if U ∼U (0,1), take U ∼U (0,1); the antithetic samples are, U = 1−U

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 11

Antithetic sampling• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R• assume fX (x) is symmetric, c being the center of symmetry

• let t(X1, . . . ,Xn) denote an estimator

• let x ∈ supp(X ); the symmetric of x w.r.t. c is x = 2c−x

• symmetry implies fX (x) = fX (x)

• sample n/2 samples X1, . . . ,Xn/2 from ∼ fX (x)

• the remaining n/2 samples X1, . . . , Xn/2 are obtained via reflection

• then t(X1, . . . ,Xn) = t(X1, . . . ,Xn/2)+ t(X1, . . . , Xn/2)

• example: if U ∼U (0,1), take U ∼U (0,1); the antithetic samples are, U = 1−U

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 11

Antithetic sampling• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R• assume fX (x) is symmetric, c being the center of symmetry

• let t(X1, . . . ,Xn) denote an estimator

• let x ∈ supp(X ); the symmetric of x w.r.t. c is x = 2c−x

• symmetry implies fX (x) = fX (x)

• sample n/2 samples X1, . . . ,Xn/2 from ∼ fX (x)

• the remaining n/2 samples X1, . . . , Xn/2 are obtained via reflection

• then t(X1, . . . ,Xn) = t(X1, . . . ,Xn/2)+ t(X1, . . . , Xn/2)

• example: if U ∼U (0,1), take U ∼U (0,1); the antithetic samples are, U = 1−U

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 11

Antithetic sampling• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R• assume fX (x) is symmetric, c being the center of symmetry

• let t(X1, . . . ,Xn) denote an estimator

• let x ∈ supp(X ); the symmetric of x w.r.t. c is x = 2c−x

• symmetry implies fX (x) = fX (x)

• sample n/2 samples X1, . . . ,Xn/2 from ∼ fX (x)

• the remaining n/2 samples X1, . . . , Xn/2 are obtained via reflection

• then t(X1, . . . ,Xn) = t(X1, . . . ,Xn/2)+ t(X1, . . . , Xn/2)

• example: if U ∼U (0,1), take U ∼U (0,1); the antithetic samples are, U = 1−U

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 11

Antithetic sampling• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R• assume fX (x) is symmetric, c being the center of symmetry

• let t(X1, . . . ,Xn) denote an estimator

• let x ∈ supp(X ); the symmetric of x w.r.t. c is x = 2c−x

• symmetry implies fX (x) = fX (x)

• sample n/2 samples X1, . . . ,Xn/2 from ∼ fX (x)

• the remaining n/2 samples X1, . . . , Xn/2 are obtained via reflection

• then t(X1, . . . ,Xn) = t(X1, . . . ,Xn/2)+ t(X1, . . . , Xn/2)

• example: if U ∼U (0,1), take U ∼U (0,1); the antithetic samples are, U = 1−U

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 11

Antithetic sampling• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R• assume fX (x) is symmetric, c being the center of symmetry

• let t(X1, . . . ,Xn) denote an estimator

• let x ∈ supp(X ); the symmetric of x w.r.t. c is x = 2c−x

• symmetry implies fX (x) = fX (x)

• sample n/2 samples X1, . . . ,Xn/2 from ∼ fX (x)

• the remaining n/2 samples X1, . . . , Xn/2 are obtained via reflection

• then t(X1, . . . ,Xn) = t(X1, . . . ,Xn/2)+ t(X1, . . . , Xn/2)

• example: if U ∼U (0,1), take U ∼U (0,1); the antithetic samples are, U = 1−U

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 11

Stratified sampling

• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R• assume, without loss of generality, that supp(X ) = [0,1]

• let t(X1, . . . ,Xn) denote an estimator

• idea: prevent samples from clustering in a particular region of the interval

• select λ ∈ (0,1)

• then draw n1 = λn samples in [0,λ ] and n2 = n−n1 = (1−λ )n samples in [λ ,1]

• t(X1, . . . ,Xn) = t(X1, . . . ,Xn1) + t(X1, . . . ,Xn2)

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 12

Stratified sampling• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R• assume, without loss of generality, that supp(X ) = [0,1]

• let t(X1, . . . ,Xn) denote an estimator

• idea: prevent samples from clustering in a particular region of the interval

• select λ ∈ (0,1)

• then draw n1 = λn samples in [0,λ ] and n2 = n−n1 = (1−λ )n samples in [λ ,1]

• t(X1, . . . ,Xn) = t(X1, . . . ,Xn1) + t(X1, . . . ,Xn2)

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 12

Stratified sampling• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R• assume, without loss of generality, that supp(X ) = [0,1]

• let t(X1, . . . ,Xn) denote an estimator

• idea: prevent samples from clustering in a particular region of the interval

• select λ ∈ (0,1)

• then draw n1 = λn samples in [0,λ ] and n2 = n−n1 = (1−λ )n samples in [λ ,1]

• t(X1, . . . ,Xn) = t(X1, . . . ,Xn1) + t(X1, . . . ,Xn2)

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 12

Stratified sampling• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R• assume, without loss of generality, that supp(X ) = [0,1]

• let t(X1, . . . ,Xn) denote an estimator

• idea: prevent samples from clustering in a particular region of the interval

• select λ ∈ (0,1)

• then draw n1 = λn samples in [0,λ ] and n2 = n−n1 = (1−λ )n samples in [λ ,1]

• t(X1, . . . ,Xn) = t(X1, . . . ,Xn1) + t(X1, . . . ,Xn2)

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 12

Stratified sampling• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R• assume, without loss of generality, that supp(X ) = [0,1]

• let t(X1, . . . ,Xn) denote an estimator

• idea: prevent samples from clustering in a particular region of the interval

• select λ ∈ (0,1)

• then draw n1 = λn samples in [0,λ ] and n2 = n−n1 = (1−λ )n samples in [λ ,1]

• t(X1, . . . ,Xn) = t(X1, . . . ,Xn1) + t(X1, . . . ,Xn2)

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 12

Stratified sampling• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R• assume, without loss of generality, that supp(X ) = [0,1]

• let t(X1, . . . ,Xn) denote an estimator

• idea: prevent samples from clustering in a particular region of the interval

• select λ ∈ (0,1)

• then draw n1 = λn samples in [0,λ ] and n2 = n−n1 = (1−λ )n samples in [λ ,1]

• t(X1, . . . ,Xn) = t(X1, . . . ,Xn1) + t(X1, . . . ,Xn2)

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 12

Stratified sampling• let X denote a (continuous) random variable with pdf ∼ fX (x), supp(X )⊂ R• assume, without loss of generality, that supp(X ) = [0,1]

• let t(X1, . . . ,Xn) denote an estimator

• idea: prevent samples from clustering in a particular region of the interval

• select λ ∈ (0,1)

• then draw n1 = λn samples in [0,λ ] and n2 = n−n1 = (1−λ )n samples in [λ ,1]

• t(X1, . . . ,Xn) = t(X1, . . . ,Xn1) + t(X1, . . . ,Xn2)

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 12

Control variates

• remember Monte Carlo integration: estimate∫ 1

0 f (x)dx via sampling

• assume there exists φ : [0,1]→ R that can be easily integrated

• therefore∫ 1

0 f (x)dx =∫ 1

0 (f (x)+φ(x)−φ(x))dx =∫ 1

0 φ(x)dx +∫ 1

0 (f (x)−φ(x))dx

• Var(f - φ ) = Var(f) + Var(φ ) - 2cov(f, φ )

• if cov(f, φ ) is high, i.e. f and φ are “similar”, Var(f - φ ) < Var(f)

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 13

Control variates• remember Monte Carlo integration: estimate

∫ 10 f (x)dx via sampling

• assume there exists φ : [0,1]→ R that can be easily integrated

• therefore∫ 1

0 f (x)dx =∫ 1

0 (f (x)+φ(x)−φ(x))dx =∫ 1

0 φ(x)dx +∫ 1

0 (f (x)−φ(x))dx

• Var(f - φ ) = Var(f) + Var(φ ) - 2cov(f, φ )

• if cov(f, φ ) is high, i.e. f and φ are “similar”, Var(f - φ ) < Var(f)

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 13

Control variates• remember Monte Carlo integration: estimate

∫ 10 f (x)dx via sampling

• assume there exists φ : [0,1]→ R that can be easily integrated

• therefore∫ 1

0 f (x)dx =∫ 1

0 (f (x)+φ(x)−φ(x))dx =∫ 1

0 φ(x)dx +∫ 1

0 (f (x)−φ(x))dx

• Var(f - φ ) = Var(f) + Var(φ ) - 2cov(f, φ )

• if cov(f, φ ) is high, i.e. f and φ are “similar”, Var(f - φ ) < Var(f)

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 13

Control variates• remember Monte Carlo integration: estimate

∫ 10 f (x)dx via sampling

• assume there exists φ : [0,1]→ R that can be easily integrated

• therefore∫ 1

0 f (x)dx =∫ 1

0 (f (x)+φ(x)−φ(x))dx =∫ 1

0 φ(x)dx +∫ 1

0 (f (x)−φ(x))dx

• Var(f - φ ) = Var(f) + Var(φ ) - 2cov(f, φ )

• if cov(f, φ ) is high, i.e. f and φ are “similar”, Var(f - φ ) < Var(f)

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 13

Control variates• remember Monte Carlo integration: estimate

∫ 10 f (x)dx via sampling

• assume there exists φ : [0,1]→ R that can be easily integrated

• therefore∫ 1

0 f (x)dx =∫ 1

0 (f (x)+φ(x)−φ(x))dx =∫ 1

0 φ(x)dx +∫ 1

0 (f (x)−φ(x))dx

• Var(f - φ ) = Var(f) + Var(φ ) - 2cov(f, φ )

• if cov(f, φ ) is high, i.e. f and φ are “similar”, Var(f - φ ) < Var(f)

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 13

Control variates• remember Monte Carlo integration: estimate

∫ 10 f (x)dx via sampling

• assume there exists φ : [0,1]→ R that can be easily integrated

• therefore∫ 1

0 f (x)dx =∫ 1

0 (f (x)+φ(x)−φ(x))dx =∫ 1

0 φ(x)dx +∫ 1

0 (f (x)−φ(x))dx

• Var(f - φ ) = Var(f) + Var(φ ) - 2cov(f, φ )

• if cov(f, φ ) is high, i.e. f and φ are “similar”, Var(f - φ ) < Var(f)

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 13

Importance sampling

• remember Monte Carlo integration: estimate∫ 1

0 f (x)dx via sampling

• standard Monte Carlo solution∫[0,1] f (x)dx ≈ I = 1

N ∑Ni=1 f (Ui), Ui ∼U (0,1)

• however, Ui are spread all over the domain

• idea: sample from another distribution gX that better captures the structure of f

•∫ 1

0 f (x)dx =∫ 1

0f (x)

gX (x)gX (x)dx =

∫ 10 h(x)gX (x)dx

• therefore, instead of sampling from the uniform distribution, sample according to gX

• variance reduced if f and gX have similar shapes

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 14

Importance sampling• remember Monte Carlo integration: estimate

∫ 10 f (x)dx via sampling

• standard Monte Carlo solution∫[0,1] f (x)dx ≈ I = 1

N ∑Ni=1 f (Ui), Ui ∼U (0,1)

• however, Ui are spread all over the domain

• idea: sample from another distribution gX that better captures the structure of f

•∫ 1

0 f (x)dx =∫ 1

0f (x)

gX (x)gX (x)dx =

∫ 10 h(x)gX (x)dx

• therefore, instead of sampling from the uniform distribution, sample according to gX

• variance reduced if f and gX have similar shapes

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 14

Importance sampling• remember Monte Carlo integration: estimate

∫ 10 f (x)dx via sampling

• standard Monte Carlo solution∫[0,1] f (x)dx ≈ I = 1

N ∑Ni=1 f (Ui), Ui ∼U (0,1)

• however, Ui are spread all over the domain

• idea: sample from another distribution gX that better captures the structure of f

•∫ 1

0 f (x)dx =∫ 1

0f (x)

gX (x)gX (x)dx =

∫ 10 h(x)gX (x)dx

• therefore, instead of sampling from the uniform distribution, sample according to gX

• variance reduced if f and gX have similar shapes

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 14

Importance sampling• remember Monte Carlo integration: estimate

∫ 10 f (x)dx via sampling

• standard Monte Carlo solution∫[0,1] f (x)dx ≈ I = 1

N ∑Ni=1 f (Ui), Ui ∼U (0,1)

• however, Ui are spread all over the domain

• idea: sample from another distribution gX that better captures the structure of f

•∫ 1

0 f (x)dx =∫ 1

0f (x)

gX (x)gX (x)dx =

∫ 10 h(x)gX (x)dx

• therefore, instead of sampling from the uniform distribution, sample according to gX

• variance reduced if f and gX have similar shapes

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 14

Importance sampling• remember Monte Carlo integration: estimate

∫ 10 f (x)dx via sampling

• standard Monte Carlo solution∫[0,1] f (x)dx ≈ I = 1

N ∑Ni=1 f (Ui), Ui ∼U (0,1)

• however, Ui are spread all over the domain

• idea: sample from another distribution gX that better captures the structure of f

•∫ 1

0 f (x)dx =∫ 1

0f (x)

gX (x)gX (x)dx =

∫ 10 h(x)gX (x)dx

• therefore, instead of sampling from the uniform distribution, sample according to gX

• variance reduced if f and gX have similar shapes

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 14

Importance sampling• remember Monte Carlo integration: estimate

∫ 10 f (x)dx via sampling

• standard Monte Carlo solution∫[0,1] f (x)dx ≈ I = 1

N ∑Ni=1 f (Ui), Ui ∼U (0,1)

• however, Ui are spread all over the domain

• idea: sample from another distribution gX that better captures the structure of f

•∫ 1

0 f (x)dx =∫ 1

0f (x)

gX (x)gX (x)dx =

∫ 10 h(x)gX (x)dx

• therefore, instead of sampling from the uniform distribution, sample according to gX

• variance reduced if f and gX have similar shapes

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 14

Importance sampling• remember Monte Carlo integration: estimate

∫ 10 f (x)dx via sampling

• standard Monte Carlo solution∫[0,1] f (x)dx ≈ I = 1

N ∑Ni=1 f (Ui), Ui ∼U (0,1)

• however, Ui are spread all over the domain

• idea: sample from another distribution gX that better captures the structure of f

•∫ 1

0 f (x)dx =∫ 1

0f (x)

gX (x)gX (x)dx =

∫ 10 h(x)gX (x)dx

• therefore, instead of sampling from the uniform distribution, sample according to gX

• variance reduced if f and gX have similar shapes

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 14

Importance sampling• remember Monte Carlo integration: estimate

∫ 10 f (x)dx via sampling

• standard Monte Carlo solution∫[0,1] f (x)dx ≈ I = 1

N ∑Ni=1 f (Ui), Ui ∼U (0,1)

• however, Ui are spread all over the domain

• idea: sample from another distribution gX that better captures the structure of f

•∫ 1

0 f (x)dx =∫ 1

0f (x)

gX (x)gX (x)dx =

∫ 10 h(x)gX (x)dx

• therefore, instead of sampling from the uniform distribution, sample according to gX

• variance reduced if f and gX have similar shapes

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 14

Quasi-Monte Carlo: alternative samplingtechniques

Quasi-Monte Carlo sampling• standard Monte Carlo: pseudo-random

samples

⇒

• quasi-Monte Carlo (QMC):deterministic samples

• in this lecture: QMC based on low discrepancy sequences• note: QMC methods defined for [0,1]d ; for any other domain, we need transformations

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 16

Quasi-Monte Carlo sampling• standard Monte Carlo: pseudo-random

samples

⇒

• quasi-Monte Carlo (QMC):deterministic samples

• in this lecture: QMC based on low discrepancy sequences• note: QMC methods defined for [0,1]d ; for any other domain, we need transformations

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 16

Quasi-Monte Carlo sampling• standard Monte Carlo: pseudo-random

samples

⇒

• quasi-Monte Carlo (QMC):deterministic samples

• in this lecture: QMC based on low discrepancy sequences

• note: QMC methods defined for [0,1]d ; for any other domain, we need transformations

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 16

Quasi-Monte Carlo sampling• standard Monte Carlo: pseudo-random

samples

⇒

• quasi-Monte Carlo (QMC):deterministic samples

• in this lecture: QMC based on low discrepancy sequences• note: QMC methods defined for [0,1]d ; for any other domain, we need transformations

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 16

Upper bound of integration error

Koksma-Hlawa inequalityRemember

• I :=∫ 1

0 f (x)dx

• If = 1N ∑

Ni=1 f (xi)

Theorem

Koksma-Hlawka inequality: |I− If | ≤ V (f )DN

• V (f )→ variation of f

• DN = supA⊂[0,1]

∣∣∣card(A)N −vol(A)

∣∣∣→ discrepancy of {xi}Ni=1

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 18

Koksma-Hlawa inequalityRemember

• I :=∫ 1

0 f (x)dx

• If = 1N ∑

Ni=1 f (xi)

Theorem

Koksma-Hlawka inequality: |I− If | ≤ V (f )DN

• V (f )→ variation of f

• DN = supA⊂[0,1]

∣∣∣card(A)N −vol(A)

∣∣∣→ discrepancy of {xi}Ni=1

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 18

Koksma-Hlawa inequalityRemember

• I :=∫ 1

0 f (x)dx

• If = 1N ∑

Ni=1 f (xi)

Theorem

Koksma-Hlawka inequality: |I− If | ≤ V (f )DN

• V (f )→ variation of f

• DN = supA⊂[0,1]

∣∣∣card(A)N −vol(A)

∣∣∣→ discrepancy of {xi}Ni=1

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 18

Low discrepancy sequences

Low discrepancy sequencesBasic idea

• in |I− If | ≤ V (f )DN , assume that V (f ) = constant

• idea: minimize error by reducing DN , i.e.

• produce {xi}Ni=1 that are “well” spaced

• in this way: O( 1√N)→ O( log(N)d

N ), where d is the dimension

⇐ ⇒

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 20

Low discrepancy sequencesBasic idea

• in |I− If | ≤ V (f )DN , assume that V (f ) = constant

• idea: minimize error by reducing DN , i.e.

• produce {xi}Ni=1 that are “well” spaced

• in this way: O( 1√N)→ O( log(N)d

N ), where d is the dimension

⇐ ⇒

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 20

Low discrepancy sequencesBasic idea

• in |I− If | ≤ V (f )DN , assume that V (f ) = constant

• idea: minimize error by reducing DN , i.e.

• produce {xi}Ni=1 that are “well” spaced

• in this way: O( 1√N)→ O( log(N)d

N ), where d is the dimension

⇐ ⇒

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 20

Low discrepancy sequences example:Halton sequences

Halton sequences• start with a prime number p

• construct a sequence based on finer and finer p-based divisions of sub-intervals of [0,1]• e.g. let p = 3− break [0,1] into 3 equal subintervals

− break each sub-interval into 3 equal subintervals

− now, the sequence is 1/3, 2/3, 1/9, 4/9, 7/9, 2/9, 5/9, 8/9− repeat until desired length

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 22

Halton sequences• start with a prime number p

• construct a sequence based on finer and finer p-based divisions of sub-intervals of [0,1]

• e.g. let p = 3− break [0,1] into 3 equal subintervals

− break each sub-interval into 3 equal subintervals

− now, the sequence is 1/3, 2/3, 1/9, 4/9, 7/9, 2/9, 5/9, 8/9− repeat until desired length

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 22

Halton sequences• start with a prime number p

• construct a sequence based on finer and finer p-based divisions of sub-intervals of [0,1]• e.g. let p = 3

− break [0,1] into 3 equal subintervals

− break each sub-interval into 3 equal subintervals

− now, the sequence is 1/3, 2/3, 1/9, 4/9, 7/9, 2/9, 5/9, 8/9− repeat until desired length

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 22

Halton sequences• start with a prime number p

• construct a sequence based on finer and finer p-based divisions of sub-intervals of [0,1]• e.g. let p = 3− break [0,1] into 3 equal subintervals

− break each sub-interval into 3 equal subintervals

− now, the sequence is 1/3, 2/3, 1/9, 4/9, 7/9, 2/9, 5/9, 8/9− repeat until desired length

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 22

Halton sequences• start with a prime number p

• construct a sequence based on finer and finer p-based divisions of sub-intervals of [0,1]• e.g. let p = 3− break [0,1] into 3 equal subintervals

− break each sub-interval into 3 equal subintervals

− now, the sequence is 1/3, 2/3, 1/9, 4/9, 7/9, 2/9, 5/9, 8/9− repeat until desired length

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 22

Halton sequences• start with a prime number p

• construct a sequence based on finer and finer p-based divisions of sub-intervals of [0,1]• e.g. let p = 3− break [0,1] into 3 equal subintervals

− break each sub-interval into 3 equal subintervals

− now, the sequence is 1/3, 2/3, 1/9, 4/9, 7/9, 2/9, 5/9, 8/9

− repeat until desired length

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 22

Halton sequences• start with a prime number p

• construct a sequence based on finer and finer p-based divisions of sub-intervals of [0,1]• e.g. let p = 3− break [0,1] into 3 equal subintervals

− break each sub-interval into 3 equal subintervals

− now, the sequence is 1/3, 2/3, 1/9, 4/9, 7/9, 2/9, 5/9, 8/9− repeat until desired length

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 22

Halton sequences example• 2D Halton grid with 100 elements

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 23

Quasi-Monte Carlo sampling: example

Model problem – damped linear oscillator

d2ydt2 (t)+c dy

dt (t)+ky(t) = f cos(ω t)y(0) = y0dydt (0) = y1

• t ∈ [0,30]

• k = 0.035

• f = 0.100

• ω = 1.000

• y0 = 0.500

• y1 = 0.000

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 25

Model problem – damped linear oscillator

d2ydt2 (t)+c dy

dt (t)+ky(t) = f cos(ω t)y(0) = y0dydt (0) = y1

• t ∈ [0,30]

• k = 0.035

• f = 0.100

• ω = 1.000

• y0 = 0.500

• y1 = 0.000

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 25

Quasi-Monte Carlo – example• t0 = 15

Deterministic result

• y(t0) =−1.51e−01

Stochastic results

• assume c ∼U (0.08,0.12)

• 100 samples, standard Monte Carlo→ E [y(t0)] =−1.61e−01, Var[y(t0)] = 6.51e−04

• 100 samples, QMC, Halton sequences→ E [y(t0)] =−1.53e−01, Var[y(t0)] = 7.78e−04

• 1000 samples, standard Monte Carlo→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.30e−04

• 1000 samples, QMC, Halton sequences→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.81e−04

• 10000 samples, standard Monte Carlo→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.84e−04

• 10000 samples, QMC, Halton sequences→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.80e−04

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 26

Quasi-Monte Carlo – example• t0 = 15

Deterministic result

• y(t0) =−1.51e−01

Stochastic results

• assume c ∼U (0.08,0.12)

• 100 samples, standard Monte Carlo→ E [y(t0)] =−1.61e−01, Var[y(t0)] = 6.51e−04

• 100 samples, QMC, Halton sequences→ E [y(t0)] =−1.53e−01, Var[y(t0)] = 7.78e−04

• 1000 samples, standard Monte Carlo→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.30e−04

• 1000 samples, QMC, Halton sequences→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.81e−04

• 10000 samples, standard Monte Carlo→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.84e−04

• 10000 samples, QMC, Halton sequences→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.80e−04

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 26

Quasi-Monte Carlo – example• t0 = 15

Deterministic result

• y(t0) =−1.51e−01

Stochastic results

• assume c ∼U (0.08,0.12)

• 100 samples, standard Monte Carlo→ E [y(t0)] =−1.61e−01, Var[y(t0)] = 6.51e−04

• 100 samples, QMC, Halton sequences→ E [y(t0)] =−1.53e−01, Var[y(t0)] = 7.78e−04

• 1000 samples, standard Monte Carlo→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.30e−04

• 1000 samples, QMC, Halton sequences→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.81e−04

• 10000 samples, standard Monte Carlo→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.84e−04

• 10000 samples, QMC, Halton sequences→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.80e−04

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 26

Quasi-Monte Carlo – example• t0 = 15

Deterministic result

• y(t0) =−1.51e−01

Stochastic results

• assume c ∼U (0.08,0.12)

• 100 samples, standard Monte Carlo→ E [y(t0)] =−1.61e−01, Var[y(t0)] = 6.51e−04

• 100 samples, QMC, Halton sequences→ E [y(t0)] =−1.53e−01, Var[y(t0)] = 7.78e−04

• 1000 samples, standard Monte Carlo→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.30e−04

• 1000 samples, QMC, Halton sequences→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.81e−04

• 10000 samples, standard Monte Carlo→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.84e−04

• 10000 samples, QMC, Halton sequences→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.80e−04

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 26

Quasi-Monte Carlo – example• t0 = 15

Deterministic result

• y(t0) =−1.51e−01

Stochastic results

• assume c ∼U (0.08,0.12)

• 100 samples, standard Monte Carlo→ E [y(t0)] =−1.61e−01, Var[y(t0)] = 6.51e−04

• 100 samples, QMC, Halton sequences→ E [y(t0)] =−1.53e−01, Var[y(t0)] = 7.78e−04

• 1000 samples, standard Monte Carlo→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.30e−04

• 1000 samples, QMC, Halton sequences→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.81e−04

• 10000 samples, standard Monte Carlo→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.84e−04

• 10000 samples, QMC, Halton sequences→ E [y(t0)] =−1.52e−01, Var[y(t0)] = 7.80e−04

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 26

Summary

Summary• the accuracy of standard Monte Carlo can be improved via− optimizing your code− increasing the number of samples− decreasing the variance of the estimators− changing the sampling technique

• variance reduction techniques− antithetic sampling− importance sampling− stratified sampling− control variates• alternative random number generation techniques− Fibonacci generators− latin hypercube sampling− Sobol’ sequences− Halton sequences

• example low-discrepancy sequences: Halton sequences

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 28

Summary• the accuracy of standard Monte Carlo can be improved via− optimizing your code− increasing the number of samples− decreasing the variance of the estimators− changing the sampling technique• variance reduction techniques− antithetic sampling− importance sampling− stratified sampling− control variates

• alternative random number generation techniques− Fibonacci generators− latin hypercube sampling− Sobol’ sequences− Halton sequences

• example low-discrepancy sequences: Halton sequences

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 28

Summary• the accuracy of standard Monte Carlo can be improved via− optimizing your code− increasing the number of samples− decreasing the variance of the estimators− changing the sampling technique• variance reduction techniques− antithetic sampling− importance sampling− stratified sampling− control variates• alternative random number generation techniques− Fibonacci generators− latin hypercube sampling− Sobol’ sequences− Halton sequences

• example low-discrepancy sequences: Halton sequences

Dr. rer. nat. Tobias Neckel | Algorithms for Uncertainty Quantification | Summer Semester 2017 28