lecture 2 probability and measurement error, part 1

Lecture 2

Probability and Measurement Error, Part 1

SyllabusLecture 01 Describing Inverse ProblemsLecture 02 Probability and Measurement Error, Part 1Lecture 03 Probability and Measurement Error, Part 2 Lecture 04 The L2 Norm and Simple Least SquaresLecture 05 A Priori Information and Weighted Least SquaredLecture 06 Resolution and Generalized InversesLecture 07 Backus-Gilbert Inverse and the Trade Off of Resolution and VarianceLecture 08 The Principle of Maximum LikelihoodLecture 09 Inexact TheoriesLecture 10 Nonuniqueness and Localized AveragesLecture 11 Vector Spaces and Singular Value DecompositionLecture 12 Equality and Inequality ConstraintsLecture 13 L1 , L∞ Norm Problems and Linear ProgrammingLecture 14 Nonlinear Problems: Grid and Monte Carlo Searches Lecture 15 Nonlinear Problems: Newton’s Method Lecture 16 Nonlinear Problems: Simulated Annealing and Bootstrap Confidence Intervals Lecture 17 Factor AnalysisLecture 18 Varimax Factors, Empircal Orthogonal FunctionsLecture 19 Backus-Gilbert Theory for Continuous Problems; Radon’s ProblemLecture 20 Linear Operators and Their AdjointsLecture 21 Fréchet DerivativesLecture 22 Exemplary Inverse Problems, incl. Filter DesignLecture 23 Exemplary Inverse Problems, incl. Earthquake LocationLecture 24 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

d=?

random variable, dno fixed value until it is realized

d=?indeterminate

d=1.04indeterminate

d=0.98

random variables have systematics

tendency to takes on some values more often than others

0 20 40 60 80 100 120 140 160 180 2001

2

3

4

5

6

7

8

N realization of data

index, i

d i 50

10

(C) (B) (A)

0 5 100

0.1

0.2

0.3

0.4

0.5

d

p(d

)

0 5 100

10

20

30

d

counts

0 5 100

0.1

0.2

0.3

0.4

0.5

d

p(d

)

0 2 4 6 8 100

0.1

0.2

0.3

0.4

0.5

d

p(d)p(d)

Δd d

probability of variable being between d and d+Δd is p(d) Δd

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?

dp(d)

dp(d)00

55

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.”

0 2 4 6 8 100

0.1

0.2

0.3

0.4

0.5

d

p(d)p(d)

ddML

Several possibilities for a typical value

point beneath the peak or “maximum likelihood point” or

“mode”

0 2 4 6 8 100

0.1

0.2

0.3

0.4

0.5

d

p(d)p(d)

ddmedianpoint dividing area in half or “median”

50% 50%

0 2 4 6 8 100

0.1

0.2

0.3

0.4

0.5

d

p(d)p(d)

d<d>balancing point or

“mean” or “expectation”

can all be different

dML ≠ dmedian ≠ <d>

formula for“mean” or “expected value”

<d>

≈ sd

ds

≈ s NsN

data

histogram

Ns

dsp≈ s P(ds)

probability distribution

step 1: usual formula for mean

step 2: replace data with its histogram

step 3: replace histogram with probability distribution.

<d><d><d>

If the data are continuous, use analogous formula containing an

integral:

≈ s P(ds)<d>

<d>

quantifying width

This function grows away from the typical value:

q(d) = (d-<d>)2so 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)

0 5 100

5

10

d

q(d)

0 5 100

0.5

1

dp(

d)

0 5 100

0.5

1

d

q(d)

*p(d

)

0 5 100

5

10

d

q(d)

0 5 100

0.5

1

d

p(d)

0 5 100

0.5

1

dq(

d)*p

(d)

(C)(B)(A)

(F)(E)(D)

variance

width is actually square root of variance, that is, σmean

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

dbar = Dd*sum(d.*p);q = (d-dbar).^2; sigma2 = Dd*sum(q.*p); sigma = sqrt(sigma2);

from tabulated p.d.f. p

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.

ddmin dmax

p(d)1/(dmax- dmin)

probability is the same everywhere in the range of possible values

box-shaped function

Large probability near the mean, d. Variance is σ2.

0 10 20 30 40 50 60 70 80 90 1000

0.02

0.04

0.06

0.08

d2σ

Gaussian (or “Normal”) p.d.f.

bell-shaped function

probability between <d>±nσGaussian p.d.f.

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 meand2 is higher or lower than its mean with equal probability

0

0.2

0.4

0.6

0.8

1

d1

d20 100

10

0

0.2

0.4

0.6

0.8

1

0.0

0.2pjoint probability density function

uncorrelated case

<d1 >

<d2 >

0

0.2

0.4

0.6

0.8

1

d1

d20 100

10

0

0.2

0.4

0.6

0.8

1

0.0

0.2p

no tendency for d2 to be either high or low when d1 is high

<d1 >

<d2 >

in uncorrelated case

joint p.d.f.is just the product of individual p.d.f.’s

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0

0.2

0.4

0.6

0.8

1

d1

d20 100

10 0.00

0.25p

2σ1

<d1 >

<d2 >

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0

0.2

0.4

0.6

0.8

1

d1

d20 100

10 0.00

0.25p

2σ1

<d1 >

<d2 >

tendency for d2 to be high when d1 is high

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0

0.2

0.4

0.6

0.8

1

d1

d20 100

10 0.00

0.25p

2σ1

2σ12σ2

θ<d1 >

<d2 >

d1

d2

2σ1

<d2><d 1>

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

d1

d2<d2><d 1>

d1

d2<d2><d 1>

d1

d2<d2>

<d 1>

(A) (B) (C)

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: variancesoff-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

d1

d2

d1

integrate over d2

integrate over d1

d2

p(d1,d2)

p(d2)

p(d1)

multivariate Gaussian (or Normal) p.d.f.

covariance matrix

mean

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.

determinant of matrix with elements ∂di/∂mj

Jacobiandeterminant

multivariate p.d.f.

rule for transforming a multivariate p.d.f.

Jacobian determinant

simple example

data with measurement

error

data analysis process

inferences with

uncertainty

one datum, duniform p.d.f.

0<d<1

m = 2d one model parameter, m

m=2d and d= m½d=0 → m=0d=1 → m=2dd/dm = ½p(d ) =1p(m ) = ½

p(d)

0 1 2 d01

p(m)

0 1 2 m01

another simple example

data with measurement

error

data analysis process

inferences with

uncertainty

one datum, duniform p.d.f.

0<d<1

m = d2 one model parameter, m

m=d2 and d=m½d=0 → m=0d=1 → m=1dd/dm = m½ -½p(d ) =1p(m ) = m½ -½

A)

B)

0.0 0.2 0.4 0.6 0.8 1.01.02.0

0.0 0.2 0.4 0.6 0.8 1.01.02.0

p(d)p(m)

d

m

multivariate example

data with measurement

error

data analysis process

inferences with

uncertainty

2 data, d1 , d2uniform p.d.f.

0<d<1

for both

m1 = d1+d2m2 = d2-d2

2 model parameters, m1 and m2

d2

d1

1-1

-11

m1=d1+d1

m2=d1-d1(A)

p(d ) = 1

m=Md with

d=M-1m so ∂d/ ∂ m = M-1 and J= |det(M-1)|= ½and |det(M)|=2

p(m ) = ½

d2

d1

1-1

-11

m1=d1+d1

m2=d1-d1

m2

m1

1-1

-112

11

d1

p(d1)

0.5 p(m1)

0-1

m1

12

-10

(A)

(B)

(C)

(D)

Note that the shape in m is different than the shape in d

d2

d1

1-1

-11

m1=d1+d1

m2=d1-d1

m2

m1

1-1

-112

11

d1

p(d1)

0.5 p(m1)

0-1

m1

12

-10

(A)

(B)

(C)

(D)

The shape is a square with sides of length √2. The amplitude is ½. So the area is ½⨉√2⨉√2 = 1

d2

d1

1-1

-11

m1=d1+d1

m2=d1-d1

m2

m1

1-1

-112

11

d1

p(d1)

0.5 p(m1)

0-1

m1

12

-10

(A)

(B)

(C)

(D)

moral

p(m) can behavior quite differently from p(d)