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ETH Chair of Structural Mechanics
Structural Identification & Health Monitoring
Probability, random variables & stochastic processes
Dr. V.K. Dertimanis & Prof. Dr. E.N. Chatzi
Outline
Fundamentals of probabilityRandom variablesStochastic processesStationary time–seriesFurther Reading
ETH Chair of Structural Mechanics 25.03.2020 2
Fundamentals of probabilityDefinitions
ζ1 ζ2 ζ3ζ4
Ωζ5ζ6
ζ7ζ8
ζ9 ζ10
Re
x2
x4x3
x1
xx
xx
ζ1 ζ2 ζ3ζ4
Ω
A=ζ5,ζ6,ζ7
ζ5ζ6
ζ7ζ8
ζ9 ζ10
• Repeated experiment that consists of a number of trials
• The result (e.g. outcome) can not be determined a priori
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Fundamentals of probabilityDefinitions
ζ1 ζ2 ζ3ζ4
Ωζ5ζ6
ζ7ζ8
ζ9 ζ10
Re
x2
x4x3
x1
xx
xx
ζ1 ζ2 ζ3ζ4
Ω
A=ζ5,ζ6,ζ7
ζ5ζ6
ζ7ζ8
ζ9 ζ10
• Each outcome ζi is defined as the elementary event
• A set A of outcomes is defined as an event
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Fundamentals of probabilityDefinitions
ζ1 ζ2 ζ3ζ4
Ωζ5ζ6
ζ7ζ8
ζ9 ζ10
Re
x2
x4x3
x1
xx
xx
ζ1 ζ2 ζ3ζ4
Ω
A=ζ5,ζ6,ζ7
ζ5ζ6
ζ7ζ8
ζ9 ζ10
• The set of all elementary events defines the sample space Ω• The sample space of an experiment may be finite, or infinite
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Fundamentals of probabilityDefinitions
Axiomatic definition: the probability P of an event is defined as thenumber that fulfills the axioms
1. P(A) ≥ 0,
2. P(Ω) = 1, and
3. P(A ∪ B) = P(A) + P(B), if A ∩ B = ∅,
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Fundamentals of probabilityDefinitions
Classical definition: the probability P of an event is defined as the ratio ofthe outcomes favorable to an event (NA) to the total number of outcomes(N)
P(A) =NA
N
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Fundamentals of probabilityDefinitions
Conditional probability
P(A|B) =P(A ∩ B)
P(B)
Mutually independent events: P(A ∩ B) = P(A)P(B)
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Fundamentals of probabilityExample 1: fair die
• Finite and discrete (e.g. countable) sample space
• Six elementary events (the number of dots on the faces)
• Possible event: A = 2, 4, 6
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Fundamentals of probabilityExample 1: fair die
• Probability of each elementary event is P(ζi ) = 1/6
• Probability of the event A: is P(A) = 1/2
• P(ζ1|A) = P(ζ1 ∩ A)/P(A) = P(∅)/P(A) = 0
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Fundamentals of probabilityExample 2: daily temperature
||Università degli Studi di Bergamo 28.02.2018 10
uncertainties
The System Identification ProblemChallenges
• Infinite and continuous (e.g. non–countable) sample space
• Elementary events: temperature values along the real axis
• Events are defined as intervals T1 ≤ T ≤ T2
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Random variablesDefinitions
Random variable: a rule for assigning to every outcome ζ of anexperiment a number
: x(ζ) : S −→ R
ζ1
Rζ2
ζ3
ζ4S
xx
x
x
x1
x2
x3
x4
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Random variablesDefinitions
Random variable: a rule for assigning to every outcome ζ of anexperiment a number: x(ζ) : S −→ R
ζ1
Rζ2
ζ3
ζ4S
xx
x
x
x1
x2
x3
x4
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Random variablesExample 3: daily temperature
||Università degli Studi di Bergamo 28.02.2018 10
uncertainties
The System Identification ProblemChallenges
• Define x(T ) = T
• Then x(T ) : R −→ R is a random variable
• A measurement is both an elementary event and a random variable
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Random variablesDefinitions
ζ1
Rζ2
ζ3
ζ4S
xx
x
x
x1
x2
x3
x4
The set x(ζ) = x1 represents a ran-dom event that contains all elementaryevents ζ for which x(ζ) = x1
The set x1 ≤ x(ζ) ≤ x2 is the randomevent that contains all elementary eventsζ for which x1 ≤ x(ζ) ≤ x2
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Random variablesDefinitions
ζ1
Rζ2
ζ3
ζ4S
xx
x
x
x1
x2
x3
x4Complete characterization of a ran-dom variable requires the calculationof probabilities associated to randomevents
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Random variablesDefinitions
ζ1
Rζ2
ζ3
ζ4S
xx
x
x
x1
x2
x3
x4
Probability distribution function:
F (x) = P(x(ζ) ≤ x)
Properties
F (x1) ≤ F (x2), if x1 ≤ x2
F (−∞) = 0
F (+∞) = 1
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Random variablesDefinitions
ζ1
Rζ2
ζ3
ζ4S
xx
x
x
x1
x2
x3
x4
Probability density function:
f (x) =dF (x)
dx
Properties
P(x1 ≤ x(ζ) ≤ x2) = F (x2)− F (x1)
=∫ x2
x1
f (x)dx
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Random variablesExpected values
ζ1
Rζ2
ζ3
ζ4S
xx
x
x
x1
x2
x3
x4
mean value:
µx = Ex(ζ) ≡∫ ∞−∞
xf (x)dx
variance:
σ2x = E[x(ζ)−µx ]2 ≡
∫ ∞−∞
[x(ζ)−µx ]2f (x)dx
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Random variablesExpected values
ζ1
Rζ2
ζ3
ζ4S
xx
x
x
x1
x2
x3
x4
mean square value:
ψ2x = Ex2(ζ) ≡
∫ ∞−∞
x2f (x)dx
basic equation:
ψ2x = σ2
x + µ2x
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Random variablesExample 4: Gaussian distributionProbability and Stochastic Processes 209
!z1
(a) The shaded area equals P (z < −z1)
z1
(b) The shaded area equals P (z < z1)
FIGURE D.2Calculation of probability for the random event z < −z1 for z ∼ N(0, 1).
Assume that the random variable x(T ) = T defined in Example D.2 follows aGaussian distribution N(µ, σ2). Substituting f(x) in Eqs. D.8a–D.8b with the ex-pression given in Tab. D.1, it follows that µx = µ and that σ2
x = σ2. Noticethat, since x(T ) ∼ N(µx, σ2
x), its behaviour is completely determined by these twoparameters.
In calculating probabilities associated with random events of a Gaussian ran-dom variable, it is customary to normalize it as z(T ) = [x(T ) − µx]/σx, sothat z(T ) ∼ N(0, 1), for which standard tables with calculated probability dis-tribution function values exist. Indicatively, if µx = 20o C and σx = 10o C,the probability of measuring a temperature below 10o C is P (x(T ) < 10) =P (z(T ) < −0.83) = 1 − P (z(T ) < 0.83) = 0.2033, or 20.33%, where the prop-erty P (z(T ) < −z1) = 1− P (z(T ) < z1) has been applied (see Figure D.2).
Consider now the case of two random variables, x(ζ) : Ωx → R and y(ζ) :Ωy → R, as shown in Figs. D.3(a)–D.3(b), with individual probability distribu-tion functions notated as Fx(x) and Fy(y), respectively. Of interest is the studyof the joint behaviour of the two random variables, which implies the calcula-tion of probabilities for joint random events (Figs. D.3(c)–D.3(d)) of the formx1 ≤ x(ζ) ≤ x2 ∩ y1 ≤ y(ζ) ≤ y2 ≡ x1 ≤ x(ζ) ≤ x2, y1 ≤ y(ζ) ≤ y2.Following the single random variable case, the joint probability distributionfunction and the joint PDF are defined as
F (x, y) = P(x(ζ) ≤ x, y(ζ) ≤ y
)(D.9)
with F (−∞, y) = F (x,−∞) = 0 and F (∞,∞) = 1, and
f(x, y) =∂2F (x, y)
∂x∂y(D.10)
respectively, with f(x, y) > 0 and∫∞−∞
∫∞−∞ f(x, y)dxdy = 1. Similarly, the
calculation of probability for the random event x(ζ) ≤ x, y(ζ) ≤ y is givenby
P(x(ζ) ≤ x, y(ζ) ≤ y
)= F (x, y) =
∫ y
−∞
∫ x
−∞f(ξ1, ξ2)dξ1dxi2 (D.11)
f (x) =1
σx√
2πe− (x−µx )2
σ2x
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Random variablesTwo random variables
210 Structural System Identification and Health Monitoring: A Primer
ζ1 ζ2 ζ3ζ4
Ωxζ5ζ6
ζ7ζ8
ζ9 ζ10
Re
x2
x4x3
x1
xx
xx
ζ1ζ2
ζ3ζ4
Ωy
ζ5
Rey3
y2
y1
y
yy
x
y
x1 x2 x3 x4
y1
y2
y3
x
y
x1x2 x3 x4
y1
y2
y3
(a) x(ζ) : Ωx → R
ζ1 ζ2 ζ3ζ4
Ωxζ5ζ6
ζ7ζ8
ζ9 ζ10
Re
x2
x4x3
x1
xx
xx
ζ1ζ2
ζ3ζ4
Ωy
ζ5
Rey3
y2
y1
y
yy
x
y
x1 x2 x3 x4
y1
y2
y3
x
y
x1x2 x3 x4
y1
y2
y3
(b) y(ζ) : Ωy → R
ζ1 ζ2 ζ3ζ4
Ωxζ5ζ6
ζ7ζ8
ζ9 ζ10
Re
x2
x4x3
x1
xx
xx
ζ1ζ2
ζ3ζ4
Ωy
ζ5
Rey3
y2
y1
y
yy
x
y
x1 x2 x3 x4
y1
y2
y3
x
y
x1x2 x3 x4
y1
y2
y3
(c) The x− y plane of random vari-ables. Each axis corresponds to therange of the random variable
ζ1 ζ2 ζ3ζ4
Ωxζ5ζ6
ζ7ζ8
ζ9 ζ10
Re
x2
x4x3
x1
xx
xx
ζ1ζ2
ζ3ζ4
Ωy
ζ5
Rey3
y2
y1
y
yy
x
y
x1 x2 x3 x4
y1
y2
y3
x
y
x1x2 x3 x4
y1
y2
y3
(d) The shaded quadrat (extendingto infinity) corresponds to the ran-dom event x < x1, y < y1
FIGURE D.3Two random variables and associated joint random events.
The individual distributions (e.g. Fx(x) and Fy(y)) and PDFs (e.g. fx(x)and fy(y)) of each random variable are referred to as marginal distributionsand PDFs, respectively. The former are recovered by the joint probabilitydistribution function as Fx(x) = F (x,∞) and Fy(y) = F (∞, y), while thelatter by the joint PDF as
fx(x) =
∫ ∞
−∞f(x, y)dy, fy(y) =
∫ ∞
−∞f(x, y)dx (D.12)
An important application of the marginal statistics pertains to the case wherethe random variables are (mutually) independent, that is, when
P(x(ζ) ≤ x, y(ζ) ≤ y
)= P
(x(ζ) ≤ x
)P(y(ζ) ≤ y
)(D.13)
implying that
F (x, y) = Fx(x)Fy(y) (D.14)
f(x, y) = fx(x)fy(y) (D.15)
ETH Chair of Structural Mechanics 25.03.2020 21
Random variablesTwo random variables
210 Structural System Identification and Health Monitoring: A Primer
ζ1 ζ2 ζ3ζ4
Ωxζ5ζ6
ζ7ζ8
ζ9 ζ10
Re
x2
x4x3
x1
xx
xx
ζ1ζ2
ζ3ζ4
Ωy
ζ5
Rey3
y2
y1
y
yy
x
y
x1 x2 x3 x4
y1
y2
y3
x
y
x1x2 x3 x4
y1
y2
y3
(a) x(ζ) : Ωx → R
ζ1 ζ2 ζ3ζ4
Ωxζ5ζ6
ζ7ζ8
ζ9 ζ10
Re
x2
x4x3
x1
xx
xx
ζ1ζ2
ζ3ζ4
Ωy
ζ5
Rey3
y2
y1
y
yy
x
y
x1 x2 x3 x4
y1
y2
y3
x
y
x1x2 x3 x4
y1
y2
y3
(b) y(ζ) : Ωy → R
ζ1 ζ2 ζ3ζ4
Ωxζ5ζ6
ζ7ζ8
ζ9 ζ10
Re
x2
x4x3
x1
xx
xx
ζ1ζ2
ζ3ζ4
Ωy
ζ5
Rey3
y2
y1
y
yy
x
y
x1 x2 x3 x4
y1
y2
y3
x
y
x1x2 x3 x4
y1
y2
y3
(c) The x− y plane of random vari-ables. Each axis corresponds to therange of the random variable
ζ1 ζ2 ζ3ζ4
Ωxζ5ζ6
ζ7ζ8
ζ9 ζ10
Re
x2
x4x3
x1
xx
xx
ζ1ζ2
ζ3ζ4
Ωy
ζ5
Rey3
y2
y1
y
yy
x
y
x1 x2 x3 x4
y1
y2
y3
x
y
x1x2 x3 x4
y1
y2
y3
(d) The shaded quadrat (extendingto infinity) corresponds to the ran-dom event x < x1, y < y1
FIGURE D.3Two random variables and associated joint random events.
The individual distributions (e.g. Fx(x) and Fy(y)) and PDFs (e.g. fx(x)and fy(y)) of each random variable are referred to as marginal distributionsand PDFs, respectively. The former are recovered by the joint probabilitydistribution function as Fx(x) = F (x,∞) and Fy(y) = F (∞, y), while thelatter by the joint PDF as
fx(x) =
∫ ∞
−∞f(x, y)dy, fy(y) =
∫ ∞
−∞f(x, y)dx (D.12)
An important application of the marginal statistics pertains to the case wherethe random variables are (mutually) independent, that is, when
P(x(ζ) ≤ x, y(ζ) ≤ y
)= P
(x(ζ) ≤ x
)P(y(ζ) ≤ y
)(D.13)
implying that
F (x, y) = Fx(x)Fy(y) (D.14)
f(x, y) = fx(x)fy(y) (D.15)
ETH Chair of Structural Mechanics 25.03.2020 22
Random variablesTwo random variables
Joint probability distribution function
F (x , y ) = P(x(ζ) ≤ x , y (ζ) ≤ y
)with F (−∞, y ) = F (x ,−∞) = 0 and F (∞,∞) = 1
Joint probability density function
f (x , y ) =∂2F (x , y )∂x∂y
ETH Chair of Structural Mechanics 25.03.2020 23
Random variablesTwo random variables
Independence
P(x(ζ) ≤ x , y (ζ) ≤ y
)= P
(x(ζ) ≤ x
)P(y (ζ) ≤ y
)
F (x , y ) = Fx (x)Fy (y )
f (x , y ) = fx (x)fy (y )
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Random variablesTwo random variables
Mean values. Define r(ζ) = [x(ζ) y (ζ)]T . Then:
µr ≡ Er(ζ) =
[Ex(ζ)Ey (ζ)
]=
[µx
µy
]
µx =∫ ∞−∞
∫ ∞−∞
xf (x , y )dxdy
µy =∫ ∞−∞
∫ ∞−∞
yf (x , y )dxdy
ETH Chair of Structural Mechanics 25.03.2020 25
Random variablesTwo random variables
Covariance matrix
Crr ≡ E(r(ζ)− µr )(r(ζ)− µr )T )
=
[E(x − µx )2 E(x − µx )(y − µy )
E(y − µy )(x − µx ) E(y − µy )2
]
=
[σ2
x cxy
cxy σ2y
]
ETH Chair of Structural Mechanics 25.03.2020 26
Random variablesTwo random variables
Covariance: a measure of the joint variability of x(ζ) and y (ζ)
cxy = cyx =∫ ∞−∞
∫ ∞−∞
[x(ζ)− µx ][y (ζ)− µy ]f (x , y )dxdy
ETH Chair of Structural Mechanics 25.03.2020 27
Stochastic ProcessesDefinitions
Stochastic Process: a rule for assigning to every outcome ζ of anexperiment a function
: xk (t) : S −→ F, F .= the set of functions x(t)
ζ1
ζ2
ζ3
ζ4S
x1[t]
x2[t]
x3[t]
x4[t]
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Stochastic ProcessesDefinitions
Stochastic Process: a rule for assigning to every outcome ζ of anexperiment a function: xk (t) : S −→ F, F .= the set of functions x(t)
ζ1
ζ2
ζ3
ζ4S
x1[t]
x2[t]
x3[t]
x4[t]
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Stochastic ProcessesPreliminaries
A stochastic process xk (t) is thus a family of functions, where eachfunction corresponds to one experimental result ζk .
Equivalently, a stochastic process is an ensemble of functions,characterized by its probability structure.
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Stochastic ProcessesPreliminaries
When both t and k are fixed, the quantity xk (t) is a number.
When t is fixed, the quantity xk (t) is a random variable.
When k is fixed, the quantity xk (t) is a sample function or a time–series.
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Stochastic ProcessesExample 5: spring–mass–damper
m
k c
u(t)
x(t)
Steady–state response:
xk (t) = Xk cos(2πft + θk )
Assume that Xk and θk are random vari-ables. Then xk (t) is a sample function(e.g. a time–series)
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Stochastic ProcessesComplete description
Requires knowledge of the joint PDFs of all (possibly infinite) randomvariables.
Times t1, t2 : f(
xk (t1), xk (t2))
= . . .
Times t1, t2, t3 : f(
xk (t1), xk (t2), xk (t3))
= . . ....
...
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Stochastic ProcessesPartial description
Requires knowledge of the mean and covariance functions
µx (t) = Exk (t) cxx (t1, t2) = E
(xk (t1)− µx (t1)
)(xk (t2)− µx (t2)
)
µy (t) = Exy (t) cyy (t1, t2) = E
(yk (t1)− µy (t1)
)(xy (t2)− µy (t2)
)
cxy (t1, t2) = E
(xk (t1)− µx (t1)
)(yk (t2)− µy (t2)
)
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Stochastic ProcessesStrong stationarity
Joint PDFs of all (possibly infinite) random variables are time invariant.
Times t1, t2 : f(
xk (t1))
= f(
xk (t2))
Times t1, t2, : f(
xk (t1), xk (t2))
= f(
xk (t1 + δt), xk (t2 + δt))
......
ETH Chair of Structural Mechanics 25.03.2020 34
Stochastic ProcessesWeak stationarity
Mean and covariance functions are time invariant
µx (t) = µx cxx (tk , tm) = cxx (tk + δt , tm + δt) = cxx (tk − tm) = cxx (h)µy (t) = µy cyy (tk , tm) = cyy (tk + δt , tm + δt) = cyy (tk − tm) = cyy (h)
cxy (tk , tm) = cxy (tk + δt , tm + δt) = cxy (tk − tm) = cxy (h)
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Stochastic ProcessesErgodicity
Strong ergodicity: all ensemble averaged statistical properties can becalculated from corresponding time averages of a single sample function(e.g. time series).
Weak ergodicity: ensemble mean and covariance functions can becalculated from corresponding time averages of a single sample function(e.g. time series).
µx (t) = µx cxx (h, k ) = cxx (h)µy (t) = µy cyy (h, k ) = cyy (h)
cxy (h, k ) = cxy (h)
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Stochastic ProcessesGaussian processes
A stochastic process is Gaussian if the associated random variables forall times follow a multidimensional normal distribution.
If a stochastic process is Gaussian and weakly stationary, then is alsostrongly stationary.
A Gaussian stochastic process is ergodic if it is weakly stationary and
1T
∫ T
−T|cxx (h)|dh −→ 0, as T −→∞
ETH Chair of Structural Mechanics 25.03.2020 37
Stochastic ProcessesExample 6: spring–mass–damper
m
k c
u(t)
x(t)
Steady–state response:
xk (t) = Xk cos(2πft + θk ), t = 0, 1, . . .
X : EX = 0 and EX 2 = 1θ : uniform distribution in [−π, π]X ,θ : independent
ETH Chair of Structural Mechanics 25.03.2020 38
Stochastic ProcessesExample 6: spring–mass–damper
m
k c
u(t)
x(t)
Mean value:
Exk (t) = E
Xk cos(2πft + θk )
= EXkE
cos(2πft + θk )
= 0
ETH Chair of Structural Mechanics 25.03.2020 39
Stochastic ProcessesExample 6: spring–mass–damper
m
k c
u(t)
x(t)
Covariance:
γxx [h] = Exk (t + h)xk (t)
= E
X 2k cos
(ω(t + h) + θk
)cos
(ωt + θk
)=
12
cos(ωh)
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Further Reading
1. Papoulis, A. (1991), Probability, Random Variables & Stochastic Processes, 3thEd., McGraw–Hill, New York, USA.
2. Bendat, J.S. and Piersol, A.G. (2010), Random Data: Analysis and MeasurementProcedures, 4th Ed., John Wiley & Sons Ltd., Chichester, UK.
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