Lecture 2
Probability and Measurement Error,
Part 1
Syllabus
Lecture 01
Lecture 02
Lecture 03
Lecture 04
Lecture 05
Lecture 06
Lecture 07
Lecture 08
Lecture 09
Lecture 10
Lecture 11
Lecture 12
Lecture 13
Lecture 14
Lecture 15
Lecture 16
Lecture 17
Lecture 18
Lecture 19
Lecture 20
Lecture 21
Lecture 22
Lecture 23
Lecture 24
Describing Inverse Problems
Probability and Measurement Error, Part 1
Probability and Measurement Error, Part 2
The L2 Norm and Simple Least Squares
A Priori Information and Weighted Least Squared
Resolution and Generalized Inverses
Backus-Gilbert Inverse and the Trade Off of Resolution and Variance
The Principle of Maximum Likelihood
Inexact Theories
Nonuniqueness and Localized Averages
Vector Spaces and Singular Value Decomposition
Equality and Inequality Constraints
L1 , L∞ Norm Problems and Linear Programming
Nonlinear Problems: Grid and Monte Carlo Searches
Nonlinear Problems: Newton’s Method
Nonlinear Problems: Simulated Annealing and Bootstrap Confidence Intervals
Factor Analysis
Varimax Factors, Empircal Orthogonal Functions
Backus-Gilbert Theory for Continuous Problems; Radon’s Problem
Linear Operators and Their Adjoints
Fréchet Derivatives
Exemplary Inverse Problems, incl. Filter Design
Exemplary Inverse Problems, incl. Earthquake Location
Exemplary Inverse Problems, incl. Vibrational Problems
Purpose of the Lecture
review random variables and their probability density
functions
introduce correlation and the multivariate Gaussian
distribution
relate error propagation to functions of random variables
Part 1
random variables and their
probability density functions
random variable, d
no fixed value until it is realized
d=?
indeterminate
d=1.04
d=?
indeterminate
d=0.98
random variables have systematics
tendency to takes on some values more often
than others
N realization of data
10
8
7
di
6
5
5
4
3
2
0
1
0
20
40
60
index, i
80
100
120
140
160
180
200
(B)
20
p(d)
counts
30
(C)
0.5
0.5
0.4
0.4
0.3
0.3
p(d)
(A)
0.2
0.2
10
0.1
0
0
5
d
10
0
0.1
0
5
d
10
0
0
5
d
10
probability of
variable
being
between d
and d+Δd
is p(d) Δd
0.5
p(d)
p(d)
0.4
0.3
0.2
0.1
0
0
2
4
Δdd
6
8
10
d
in general
probability is the integral
probability that d is between
d1 and d2
the probability that d has some value is
100% or unity
probability that d is between
its minimum and maximum
bounds, dmin and dmax
How do these two p.d.f.’s differ?
p(d)
d
0
5
p(d)
0
d
5
Summarizing a probability density
function
typical value
“center of the p.d.f.”
amount of scatter around the typical value
“width of the p.d.f.”
Several possibilities for a typical value
0.5
p(d)
p(d)
0.4
0.3
0.2
0.1
0
0
2
dML
4
6
d
8
point beneath the
peak or “maximum
likelihood point” or
“mode”
10
d
0.5
p(d)
p(d)
0.4
0.3
0.2
0.1
0
50%
0
2
50%
4
dmedian
6
8
10
d
d
point dividing area
in half or “median”
0.5
p(d)
p(d)
0.4
0.3
0.2
0.1
0
0
2
4
<d>
6
8
10
d
d
balancing point or
“mean” or “expectation”
can all be different
dML ≠ dmedian ≠ <d>
formula for
“mean” or “expected value”
<d>
step 1: usual formula for mean
d
<d>
data
step 2: replace data with its histogram
<d>
≈
s
Ns
ds
histogram
step 3: replace histogram with probability distribution.
<d>
≈
≈
s
s
Ns
N
P(ds)
p
ds
probability distribution
If the data are continuous, use
analogous formula containing an
integral:
<d>
<d>
≈
s
P(ds)
quantifying width
This function grows away from the typical value:
q(d) = (d-<d>)2
so the function q(d)p(d) is
small if most of the area is near <d> , that is, a narrow p(d)
large if most of the area is far from <d> , that is, a wide p(d)
so quantify width as the area under q(d)p(d)
(A)
(B)
5
0
(D)
5
d
0
10
5
d
5
0
5
d
10
0
(F)
5
d
10
5
d
10
1
0.5
0
0.5
0
10
1
p(d)
q(d)
0
(E)
10
0
0.5
q(d)*p(d)
0
1
q(d)*p(d)
1
p(d)
q(d)
10
(C)
0
5
d
10
0.5
0
0
variance
mean
width is actually square root of variance, that is, σ
estimating mean and variance from data
estimating mean and variance from data
usual formula
for “sample
mean”
usual formula
for square of
“sample
standard
deviation”
MabLab scripts for mean and variance
from tabulated p.d.f. p
dbar = Dd*sum(d.*p);
q = (d-dbar).^2;
sigma2 = Dd*sum(q.*p);
sigma = sqrt(sigma2);
from realizations of data
dbar = mean(dr);
sigma = std(dr);
sigma2 = sigma^2;
two important probability density
functions:
uniform
Gaussian (or Normal)
uniform p.d.f.
p(d)
box-shaped function
1/(dmax- dmin)
d
dmin
dmax
probability is the same everywhere
in the range of possible values
Gaussian (or “Normal”) p.d.f.
0.08
0.06
bell-shaped function
0.04
2σ
0.02
0
0
10
20
30
40
50
60
70
80
90
100
d
Large probability near the mean, d.
Variance is σ2.
Gaussian p.d.f.
probability between <d>±nσ
Part 2
correlated errors
uncorrelated random variables
no pattern of between values of one
variable and values of another
when d1 is higher than its mean
d2 is higher or lower than its mean
with equal probability
joint probability density function
uncorrelated case
0
0
<d2 >
1
10
d2
p
0.2
1
0.8
0.8
0.6
0.6
<d1 >
0.4
0.4
0.2
0.2
0.0
10
0
d1
0
0
0
<d2 >
1
10
d2
p
0.2
1
0.8
0.8
0.6
0.6
<d1 >
0.4
0.4
0.2
0.2
0.0
10
0
d1
no tendency for d2 to be
0 either
high or low when d1 is high
in uncorrelated case
joint p.d.f.
is just the product of individual p.d.f.’s
0
<d2 >
0
1
10
d2
p
0.9
0.25
1
0.8
0.8
0.7
0.6
0.6
0.5
<d1 >
0.4
0.4
0.3
0.2
0.2
10
2σ1
d1
0.00
0
0.1
0
0
<d2 >
0
1
10
d2
p
0.9
0.25
1
0.8
0.8
0.7
0.6
0.6
0.5
<d1 >
0.4
0.4
0.3
0.2
0.2
10
2σ1
d1
0.00
0
tendency for d2 to be
high when d1 is high
0.1
0
<d2 >
0
0
θ
1
10
d2
p
0.9
0.25
1
0.8
0.8
0.7
0.6
0.6
<d1 >
0.5
2σ1
0.4
0.4
0.3
0.2
2σ2
10
2σ1
d1
0.2
0.00
0
0.1
0
<d2>
<d1>
d2
d1
2σ1
d1
d2
(B)
d1
<d2>
d2 (C)
<d1>
<d2>
<d1>
<d1>
(A)
d1
<d2>
d2
formula for covariance
+ positive correlation high d1 high d2
- negative correlation high d1 low d2
joint p.d.f.
mean is a vector
covariance is a symmetric matrix
diagonal elements: variances
off-diagonal elements: covariances
estimating covariance from a table D of
data
Dki: realization k of data-type i
in MatLab, C=cov(D)
univariate p.d.f. formed from joint p.d.f.
p(d) → p(di)
behavior of di irrespective of the other ds
integrate over
everything but di
p(d1,d2)
d2
p(d1)
integrate
over d2
d1
p(d2)
integrate over
d1
d2
d1
multivariate Gaussian (or Normal) p.d.f.
mean
covariance
matrix
Part 3
functions of random variables
functions of random variables
data with
measurement
error
data analysis
process
inferences
with
uncertainty
functions of random variables
given p(d)
with m=f(d)
what is p(m) ?
univariate p.d.f.
use the chair rule
univariate p.d.f.
use the chair rule
rule for
transforming a
univariate p.d.f.
multivariate p.d.f.
Jacobian
determinant
determinant
of matrix
with
elements
∂di/∂mj
multivariate p.d.f.
Jacobian
determinant
rule for
transforming a
multivariate
p.d.f.
simple example
data with
measurement
error
one datum, d
uniform p.d.f.
0<d<1
data analysis
process
inferences
with
uncertainty
m = 2d
one model
parameter, m
m=2d and d=½m
d=0 → m=0
d=1 → m=2
dd/dm = ½
p(d ) =1
p(m ) = ½
p(d)
1
0
d
0
p(m)
1
2
1
0
m
0
1
2
another simple example
data with
measurement
error
one datum, d
uniform p.d.f.
0<d<1
data analysis
process
m=
d2
inferences
with
uncertainty
one model
parameter, m
m=d2 and d=m½
d=0 → m=0
d=1 → m=1
dd/dm = ½m-½
p(d ) =1
p(m ) = ½ m-½
A)
p(d)
2.0
1.0
0.0
0.2
0.4
0.2
0.4
d
0.6
0.8
1.0
0.6
0.8
1.0
p(m)
B)
2.0
1.0
0.0
m
multivariate example
data with
measurement
error
2 data, d1 , d2
uniform p.d.f.
0<d<1
for both
data analysis
process
inferences
with
uncertainty
m1 = d1+d2
m2 = d2-d2
2 model
parameters,
m1 and m2
(A)
m2=d1-d1
-1
-1
1
d2
1
m1=d1+d1
d1
m=Md with
and |det(M)|=2
d=M-1m so ∂d/ ∂ m = M-1 and J= |det(M-1)|= ½
p(d ) = 1
p(m ) = ½
(C)
(A)
m2=d1-d1
-1
-1
1
-1
1
d2
0
p(d1)
1
1
m1=d1+d1
d1
d1
(B)
(D)
-1
-1
1
-1
0.5
m2
1
1
2
2
m1
)
Notep(mthat
the
shape in m is
different than
the shape in d
0
1
m1
(C)
(A)
m2=d1-d1
-1
-1
1
-1
1
d2
0
p(d1)
1
1
m1=d1+d1
d1
d1
(B)
(D)
-1
-1
1
-1
m2
0
1
1
2
2
m1
The
shape is a square
0.5
p(m )
with sides of length
√2. The amplitude is
½. So the area is
½⨉√2⨉√2 = 1
1
m1
(C)
(A)
m2=d1-d1
-1
-1
1
-1
1
d2
0
p(d1)
1
1
m1=d1+d1
d1
d1
(B)
(D)
-1
-1
1
-1
0.5
m2
0
1
1
2
2
m1
m1
p(m1)
moral
p(m) can behavior quite differently from p(d)