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Physics Fluctuomatics (Tohoku University) 1
Physical Fluctuomatics3rd Random variable, probability distribution and
probability density function
Kazuyuki TanakaGraduate School of Information Sciences, Tohoku University
[email protected]://www.smapip.is.tohoku.ac.jp/~kazu/
Physics Fluctuomatics (Tohoku University) 2
Probability
a. Event and Probabilityb. Joint Probability and Conditional
Probabilityc. Bayes Formula, Prior Probability and
Posterior Probabilityd. Discrete Random Variable and
Probability Distributione. Continuous Random Variable and
Probability Density Functionf. Average, Variance and Covarianceg. Uniform Distributionh. Gauss Distribution
Last Talk
Present Talk
Physics Fluctuomatics (Tohoku University) 3
Probability and Random Variable
We introduce a one to one mapping X(A) from every events A to a mutual different real number. The mapping X(A) is referred to as Random Variable of A. The random variable X(A) is often denoted by just the notation X.Probability of the event X=x that the random variable X takes a real number x is denoted by Pr{X=x}. Here, x is referred to as the state of the random variable X . The set of all the possible states is referred to as State Space.
If events X=x and X=x’ are exclusive of each other, the states x and x’ are excusive of each other.
Physics Fluctuomatics (Tohoku University) 4
Discrete Random Variable and Continuous Random Variable
Discrete Random Variable: Random Variable in Discrete State Space Example:{x1,x2,…,xM}
Continuous Random Variable: Random Variable in Continuous State Space Example : (−∞,+∞)
Physics Fluctuomatics (Tohoku University) 5
Discrete Random Variable and Probability Distribution
MxxxxxPxX ,,, Pr 21
If all the probabilities for the events X=x1, X=x2,…, X=xM are expressed in terms of a function P(x) as follows:
the function P(x) and the variable x is referred to as Probability Distribution and State Variable, respectively.
Random Variable State Variable State
Let us suppose that the sample W is expressed by Ω=A1∪A2 …∪ ∪AM where every pair of events Ai and Aj are exclusive of each other.We introduce a one to one mapping X:Ai xi (i=1,2,…,M).
Physics Fluctuomatics (Tohoku University) 6
Discrete Random Variable and Probability Distribution
MixP i ,,2,1 10
M
iixP
1
1
Probability distributions have the following properties:
Normalization Condition
Physics Fluctuomatics (Tohoku University) 7
Average and Variance
M
iii xPxXE
1
Average of Random Variable X : μ
M
iii xPxXV
1
22
Variance of Random Variable X: σ2
s : Standard Deviation
Physics Fluctuomatics (Tohoku University) 8
Discrete Random Variable and Joint Probability Distribution
yxPyYxX ,,Pr
If the joint probability Pr{(X=x)∩(Y=y)}= Pr{X=x,Y=y} is expressed in terms of a function P(x,y) as follows:
P(x,y) is referred to as Joint Probability Distribution.
Probability Vector
Y
XState Vector
y
x
Physics Fluctuomatics (Tohoku University) 9
Discrete Random Variable and Marginal Probability Distribution
M
iiY yxPyP
1
,Marginal Probability Distribution
xY yxPyP ,
Summation over all the possible events in which every pair of events are exclusive of each other.
Simplified Notation
1),( x y
yxP Normalization Condition
Let us suppose that the sample W is expressed by Ω=A1∪A2 …∪∪AM where every pair of events Ai and Aj are exclusive of each other.
We introduce a one to one mapping X:Ai xi (i=1,2,…,M).
Physics Fluctuomatics (Tohoku University) 10
Discrete Random Variable and Marginal Probability
x z u
Y uzyxPyP ,,,
Marginal Probability Distribution
X Y
Z U
Marginalize
Marginal Probability of High Dimensional Probability Distribution
Physics Fluctuomatics (Tohoku University) 11
Independency of Discrete Random Variable
If random variables X and Y are independent of each others,
yPxPyxP 21,
Joint Probability Distribution of Random Variables X and Y Probability Distribution of
Random Variable X
Probability Distrubution of Random Variable Y
yPyxPyPx
Y 2, Marginal Probability Distribution of Random Varuiable Y
1)()( 21 yx
yPxP
Physics Fluctuomatics (Tohoku University) 12
Covariance of Discrete Random Variables
M
i
N
jjiYjXi yxPyxYX
1 1
,,Cov
Covariance of Random Variables X and Y
M
i
N
jjiiX yxPxX
1 1
,]E[
M
i
N
jjiiY yxPyY
1 1
,]E[
]V[],Cov[
],Cov[]V[
YXY
YXXR
][],Cov[ XVXX ][],Cov[ YVYY
Covariance Matrix
Physics Fluctuomatics (Tohoku University) 13
Example of Probability Distribution
aX tanhE
2tanh1V aX
1
cosh2
exp)( x
a
axxP
a
E[X]
0
Physics Fluctuomatics (Tohoku University) 14
Example of Joint Probability Distributions
a
XYYX
tanh
E],Cov[
1V X
1 ,1
cosh4
exp),( yx
a
axyyxP
a
Cov[X,Y]
0
0E X
Physics Fluctuomatics (Tohoku University) 15
Example of Conditional Probability Distribution
a
axyppxyP yxyx
cosh2
exp1)( ,,1
1 ,1 yx
p
pa
1ln
2
1
Conditional Probability of Binary Symmetric Channel
Physics Fluctuomatics (Tohoku University) 16
Continuous Random Variable and Probability Density Function
aXbXbXa PrPrPr
For a random variable X defined in the state space (−∞,+∞), the probability that the state x is in the interval (a,b) in expressed as
xXxF Pr Distribution Function
b
adxxaFbFbXa Pr
dx
xdFx Probability Density Function
Physics Fluctuomatics (Tohoku University) 17
Continuous Random Variable and Probability Density Function
xx 0
1
dxx
Normalization Condition
Physics Fluctuomatics (Tohoku University) 18
Average and Variance of Continuous Random Variable
dxxxXE
Average of Random Variable X
dxxxXV 22
Variance of Random Variable X
Physics Fluctuomatics (Tohoku University) 19
Continuous Random variables and Joint Probability Density Function
確率変数 X と Y の状態空間 (−∞,+∞) において状態 x と y が区間 (a,b)×(c,d) にある確率
d
c
b
adxdyyx
dYcbXa
,
Pr
Joint Probability Density Function
1, dxdyyx Normalization Condition
For random variables X and Y defined in the state space (−∞,+∞), the probability that the state vector (x,y) is in the region (a,b)(c,d) is expressed as
Physics Fluctuomatics (Tohoku University) 20
Continuous Random Variables and Marginal Probability Density Function
Marginal Probability Density Function of Random Variable Y
dxyxyY
,
Physics Fluctuomatics (Tohoku University) 21
Independency of Continuous Random Variables
Random variables X and Y are independent of each other.
yxyx 21,
Joint Probability Density Function of X and Y
Probability Density Function of Y
ydxyxyY 2,
Marginal Probability Density Function Y
1)(
1)(
2
1
dyy
dxx
Probability Density Function of X
Physics Fluctuomatics (Tohoku University) 22
Covariance of Continuous Random Variables
dxdyyxyxYX YX
,,Cov
Covariance of Random Variables X and Y
dxdyyxxXX
,]E[
]V[],Cov[
],Cov[]V[
YXY
YXXR
][],Cov[ XVXX ][],Cov[ YVYY
Covariance Matrix
dxdyyxyYY
,]E[
Physics Fluctuomatics (Tohoku University) 23
Uniform Distribution U(a,b)
xbax
bxaabx
,0
1
2
Eba
X
12
V2ab
X
Probability Density Function of Uniform Distribution
p(x)
x0 a b
(b-a)-1
Physics Fluctuomatics (Tohoku University) 24
Gauss Distribution N(μ,σ2)
2
22 2
1exp
2
1
xx
XE 2V X
2
2
1exp 2 d
The average and the variance are derived by means of Gauss Integral Formula
Probability Density Function of Gauss Distribution with average μ and variance σ2
xp(x)
μ x0
)0(
Physics Fluctuomatics (Tohoku University) 25
Multi-Dimensional Gauss Distribution
Y
XYX y
xyxyx
1
2,
2
1exp
det2
1, C
C
CC det2
2
1exp 1T dd
by using the following d -dimentional Gauss integral formula
For a positive definite real symmetric matrix C, two-Dimensional Gaussian Distribution is defined by
yx ,
C
]V[],Cov[
],Cov[]V[
YXY
YXXThe covariance matrix is given in terms of the matrix C as follows:
Physics Fluctuomatics (Tohoku University) 26
Law of Large Numbers
)( )(1
21 nXXXn
Y nn
Let us suppose that random variables X1,X2,...,Xn are identical and mutual independent random variables with average . m Then we have
Central Limit Theorem
)(1
21 nn XXXn
Y
tends to the Gauss distribution with average m and variance s2/n as n+.
We consider a sequence of independent, identical distributed random variables, {X1,X2,...,Xn}, with average m and variance s2. Then the distribution of the random variable
Physics Fluctuomatics (Tohoku University) 27
Summary
a. Event and Probabilityb. Joint Probability and Conditional
Probabilityc. Bayes Formula, Prior Probability and
Posterior Probabilityd. Discrete Random Variable and
Probability Distributione. Continuous Random Variable and
Probability Density Functionf. Average, Variance and Covarianceg. Uniform Distributionh. Gauss Distribution
Last Talk
Present Talk
Physics Fluctuomatics (Tohoku University) 28
Practice 3-1
1
cosh2
exp)( x
a
axxP
Let us suppose that a random variable X takes binary values 1 and the probability distribution is given by
Derive the expression of average E[X] and variance V[X] and draw their graphs by using your personal computer.
Physics Fluctuomatics (Tohoku University) 29
Practice 3-2
1 ,1
cosh4
exp),( yx
a
axyyxP
Derive the expressions of Marginal Probability Destribution of X, P(X), and the covariance of X and Y, Cov[X,Y].
Let us suppose that random variables X and Y take binary values 1 and the joint probability distribution is given by
Physics Fluctuomatics (Tohoku University) 30
Practice 3-3
yxyx ppxyP ,, 1)( 1
p
pa
1ln
2
1 a
axyxyP
cosh2
exp)(
Show that it is rewritten as
Hint 1 ,1 12
1, yxxyyx pp lnexp
cosh(c) is an even function for any real number c.
Let us suppose that random variables X and Y take binary values 1 and the conditional probability distribution is given by
Physics Fluctuomatics (Tohoku University) 31
Practice 3-4
0
2
22
1exp
d
Prove the Gauss integral formula:
1
2
0 0
22
0
222
2
1explim2
2
1
2
1explim2
2
1explim2
2
1explim
2
1exp
rR
R R
R
R
R
R
RR
rdrdd
ddd
Hint
Physics Fluctuomatics (Tohoku University) 32
Practice 3-5
2
22 2
1exp
2
1
xxp
XE 2V X
Prove that the average E[X] and the variance V[X] are given by
Let us suppose that a continuous random variable X takes any real number and its probability density function is given by
x
Draw the graphs of p(x) for μ=0, σ=10, 20, 40 by using your personal computer.
Physics Fluctuomatics (Tohoku University) 33
Practice 3-6
Make a program for generating random numbers of uniform distribution U(0,1). Draw histgrams for N generated random numbers for N=10, 20, 50, 100 and 1000.
rand()randmax
1x
In the C language, you can use the function rand() that generate one of values 0,1,2,…,randmax, randomly. Here, randmax is the maximum value of outputs of rand().
Physics Fluctuomatics (Tohoku University) 34
Practice 3-7
Make a program that generates random numbers of Gauss distribution with average m and variance σ2. Draw histgrams for N generated random numbers for N=10, 20, 50, 100 and 1000.
For n random numbers x1,x2,…,xn generated by any probability distribution, (x1+x2+…+xn )/n tends to the Gauss distribution with average m and variance σ2/n for sufficient large n. [Central Limit Theorem]
61221 xxx
First we have to generate twelve uniform random numbers x1,x2,…,x12 in the interval [0,1].
Gauss random number with average 0 and variaince 1
σξ+μ generate Gauss random numbers with average μ and variance σ2
Hint:
Physics Fluctuomatics (Tohoku University) 35
Practice 3-8
CC det2
2
1exp 1T dd
For any positive integer d and d d positive definite real symmetric matrix C, prove the following d-dimensional Gauss integral formulas:
13
2
1
000
000
000
000
UUC
d
duuuU
,,, 11
By using eigenvalues λi and their corresponding eigenvectors (i=1,2,…,d) of the matrix C, we haveiu
Hint:
Physics Fluctuomatics (Tohoku University) 36
Practice 3-9
xxxp
d
1T
2
1exp
det2
1C
C
d
dx
x
x
x ,2
1
Prove that the average vector is and the covariance matrix is C.
We consider continuous random variables X1,X2,…,,Xd. The joint probability density function is given by