Distribution models

Transcription

Distribution models

Distribution models
3
Ignacio Cascos
Depto. Estadística, Universidad Carlos III
1
Outline
1. Discrete distributions
Binomial distribution
Geometric distribution
Poisson distribution
2. Continuous distributions
Uniform distribution
Exponential distribution
Normal distribution
Central Limit Theorem
3. Multivariate normal distribution
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A random experiment consists of n trials such that: The trials
are independent. Each trial results either in success or failure.
The probability of a success, p, remains constant.
The random variable that equals the number of
trials that result in a success follows a binomial
distribution with parameters 0< p <1 and n=1,2,…
X ~ B(n, p)
X≡ number of (indep.) trials that result in a success
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Probability mass function:
 n k
n−k


P( X = k ) =   p (1 − p) , k ∈ {0,1, K , n}.
k
It is possible to write X=X1+…+Xn where Xi ~ B(1, p)
independent random variables.
Parameters:
E[X] = np ; Var[X] = np(1−p)
If X~B(n1, p) and Y~B(n2, p) are indep, X+Y~B(n1+n2, p)
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B(50,0'7)
0.00
0.00
0.05
0.02
0.10
0.04
0.15
0.06
0.20
0.08
0.25
0.10
0.30
0.35
0.12
B(5,0'7)
0
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2
3
4
5
0 3 6 9
13 17 21 25 29 33 37 41 45 49
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Example (Four flamates and a die)
Four flatmates Alice, Bob, Charly, and Dave roll a
6-sided die every night in order to decide who washes
the dishes after dinner. If the outcome is 1, Alice
washes, it is its 2, Bob does, while for 3 or 4 Charly
must wash the dishes and for 5 or 6 it is Dave's turn.
a) What is the probability that Charly washes the
dishes at most twice in a week (7 days)?
b) How many days (dinners) must we wait on
average until it is Alice's turn to wash the dishes?
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Outline
Normal distribution
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In a series of independent trials with constant
probability of a success, 0<p<1, the random
variable denoting the number of trials until the
first success follows a geometric distribution
with parameter p
X ~ G(p)
X≡ number of (indep) trials until the first success
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P( X = k ) = (1 − p )
k −1
p, k ∈ {1,2,3, K}.
Parameters: E[X] = 1/p ; Var[X] = (1−p)/p2
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G(0'3)
0.0
0.00
0.05
0.1
0.10
0.2
0.15
0.3
0.20
0.4
0.25
0.5
0.30
G(0'5)
1
2
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4
5
6
7
8
9 10
12
14
1
2
3
4
5
6
7
8
9 10
12
14
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a) What is the probability that Charly washes the
dishes at most twice in a week (7 days)?
b) How many days (dinners) must we wait on
average until it is Alice's turn to wash the dishes?
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Outline
Normal distribution
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Assume that certain events occur in a fixed interval of real
numbers (period of time, area, volume,…) with a known
average rate λ>0 and independently one from the others.
The random variable that equals the number of events
occurring in the interval follows a Poisson distribution
with parameter λ,
X ~ ℘(λ)
X≡ number of events in the interval
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P( X = k ) = e
Parameters:
−λ
λ
k
k!
, k ∈ {0,1,2,K}.
E[X] = λ ; Var[X] = λ
If X~℘(λ1) and Y~℘(λ2) are indep, X+Y~℘(λ1+λ2)
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P(3)
0.00
0.00
0.05
0.05
0.10
0.15
0.10
0.20
0.15
0.25
0.30
0.20
0.35
P(1)
0
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2
3
4
5
6
7
8
9
10
0
1
2
3
4
5
6
7
8
9
10
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Example (radioactive material)
A sample of radioactive material emits, on average,
15 alpha particles per minute. If the number of alpha
particles emitted follows a Poisson distribution, what is
the probability of 10 alpha particles being emitted in:
a) 1 minute ?
b) 2 minutes ?
c) Many years later, the material averages 6 alpha
particles emitted per min. What is the probability of
at least 6 alpha particles being emitted in 1 minute?
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Outline
Normal distribution
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(Continuous) Uniform distribution
A random variable uniformly distributed in the
interval (a,b) represents a number chosen at
random between a and b. The selection is made
in such a way that the probability that the
random variable lays in any interval inside (a,b)
depends only on the length of such interval,
X~U(a,b)
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Density mass function:
 b −1 a if
f ( x) = 
 0 if
x ∉ ( a, b)
Cumulative distribution function:
0
 x−a
F ( x) =  b − a
1

x ∈ ( a, b)
Parameters:
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if
x≤a
if
a< x<b
if
x≥b
E[X] = (a+b)/2 ; Var[X] = (b−a)2/12
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Uniform Distribution
1
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
Lower limit,Upper limit
1,3
cumulative probability
density
Uniform Distribution
Lower limit,Upper limit
1
1,3
0,8
0,6
0,4
0,2
0
0 0,4 0,8 1,2 1,6 2 2,4 2,8 3,2 3,6 4
0 0,4 0,8 1,2 1,6 2 2,4 2,8 3,2 3,6 4
x
x
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Outline
Normal distribution
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The random variable that equals the distance
between successive events in a Poisson process
with mean λ>0 follows an exponential
distribution with parameter λ,
X ~ Exp(λ)
X≡ distance between successive events
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Density mass function:
λe − λx
f ( x) = 
 0
x>0
x≤0
Cumulative distribution function:
1 − e − λx
F ( x) = 
 0
if
if
Parameters:
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if
if
x>0
x≤0
E[X] = λ−1 ; Var[X] = λ−2
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Exponential Distribution
0,1
Mean
10
density
0,08
0,06
0,04
0,02
0
0
10
20
30
40
50
60
Exponential Distribution
1
Mean
10
0,8
0,6
0,4
0,2
0
-10
0
x
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10
20
30
40
50
60
70
x
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Lack of memory property.
For an exponential random variable T,
given t1,t2>0
P(T > t1+t2 | T > t1) = P(T > t2)
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On average, a sample of radioactive material emits
15 alpha particles per minute.
a) What is the average time between the emission of
two alpha particles?
b) What is the probability that the time between the
emission of two alpha particles is longer than 10 sec?
c) Last alpha particle was emitted 10 seconds ago.
What is the probability that it still takes longer than
10 seconds until the next particle is emitted?
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Outline
Normal distribution
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Normal distribution
The most widely used model for the distribution
of a random variable is a normal (or Gaussian)
distribution. Apart from other relevant
properties, it appears as the limit distribution in
the Central Limit Theorem. A normal
distribution is determined by two parameters, the
mean µ and the standard deviation σ>0,
X ~ N(µ,σ)
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Normal distribution
Standard normal density mass function N(0,1):
 x2 
1
exp− 
f ( x) =
2π
 2
Density mass function N(µ,σ):
 ( x − µ )2 
1
exp−
f ( x) =

2
2σ 
σ 2π

Parameters:
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E[X] = µ ; Var[X] = σ2
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Normal distribution
Normal Distribution
0,4
Mean,Std. dev.1
0,1
0,8
0,3
density
Normal Distribution
0,2
0,1
0
-5
-3
-1
1
3
5
Mean,Std. dev.
0,1
0,6
0,4
0,2
0
-5
-3
x
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-1
1
3
5
x
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Normal distribution
N(0,1) negro, N(2,1) rojo
0.2
0.1
0.0
0.0
0.2
0.4
0.6
0.3
0.8
0.4
N(0,0'5) rojo, N(0,1) negro, N(0,2) azul
-6
-4
-2
0
2
4
6
-6
r
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-4
-2
0
2
4
6
r
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Normal distribution
1.
2.
Properties.
If X ~ N(µ,σ) , for every a and b,
aX+b ~ N(aµ+b , |a|σ)
If X ~ N(µ
µ1,σ
σ1) , Y ~ N(µ
µ2,σ
σ2) indep, for a, b
aX+bY ~ N(aµ1+bµ2 , (a2σ12+b2σ22)1/2)
Standardization. Given X~N(µ,σ), the random
variable (X−µ)/σ follows a standard normal
distribution, N(0,1).
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Table for the N(0,1) cdf
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Example (Capacitors)
A machine makes capacitors with a mean value
of 25 µF and a standard deviation of 6 µF.
Assuming that capacitance follows a Gaussian
distribution, find the probability that the value of
capacitance exceeds 31 µF.
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Given n independent random variables X1,X2,…,Xn, with
finite means and variances E[Xi]=µi and Var[Xi]=σi2, the
limiting distribution (n→∞) of their sum is normal
X1+X2+…+Xn≈N(Σi=1,nµi , (Σi=1,nσi2)1/2)
The approximation is usually good for n > 30.
If the variables are discrete, we will use a correction
factor called continuity correction.
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Normal approximations
Normal approximation to the Binomial
distribution. A Binomial distribution B(n,p) with
n > 30 and np(1−p) > 5, is approximately
(
N np , np (1 − p )
)
0.00
0.04
0.08
0.12
B (5 0 ,0 '7 ) y N (3 5 ,3 '2 4 )
0
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6
9
1 3
1 7
2 1
2 5
2 9
3 3
3 7
4 1
4 5
4 9
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c) How many days (dinners) must we wait until Bob
washes the dishes at least 11 times with probability
0.95?
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Normal approximations
Normal approximation to the Poisson
distribution. A Poisson distribution℘(λ) with λ>5
is approximately N(λ, λ1/2)
0.00
0.02
0.04
P (4 9 ) y N (4 9 ,7 )
0
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13
21
29
37
45
53
61
69
77
85
93
38
On average, a sample of radioactive material emits
15 alpha particles per minute. What is the approximate
probability of 10 alpha particles being emitted in:
a) 1 minute ?
b) Many years later, the material averages 6 alpha
particles emitted per min. What is the probability of
at least 6 alpha particles being emitted in 1 minute?
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39
Outline
Normal distribution
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Bivariate normal distribution
The density mass function of a random vector that
follows a bivariate normal distribution with mean vector
(µ1,µ2) and covariance matrix Σ is
 1
x − µ1 
1
−1  1

f ( x1 , x2 ) =
exp− (x1 − µ1 , x2 − µ 2 )Σ 
1/ 2
2π Σ
 x2 − µ 2  
 2
 σ 12
if Σ = 
 ρσ 1 σ 2
ρσ 1 σ 2 
 , then
2
σ 2 
 − 1
1
exp
2
2
−
2
1
ρ
2πσ 1σ 2 1 − ρ

)
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(
 x − µ  2  x − µ  2
 x1 − µ1  x2 − µ 2  
1
1
2
2
 + 
 − 2 ρ 

 

 σ 1   σ 2 
 σ 1  σ 2  
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If X1 and X2 have a bivariate normal distribution
with mean vector (µ1,µ2) and covariance matrix
Σ, the marginal distributions of X1 and X2 are
normal,
X1~N(µ1,σ1) y X2~N(µ2,σ2) .
The correlation ρ measures the dependence between the
variables.
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rho=0, sigma1)1, sigma2=3
-3
-10
-2
-5
-1
0
0
1
5
2
10
rho=0, sigma1=sigma2
-1
0
1
2
3
rho=0.8, sigma1=sigma2
-10
-5
0
rho=-0.8, sigma1=sigma2
10
0
-5
0
-5
-4
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5
-2
5
-3
-2
0
2
4
-4
-2
0
2
4
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Properties. Given (X1,X2) a normal random
vactor with mean vector (µ1,µ2) and
covariance matrix
 σ 12
ρσ 1 σ 2 

Σ = 
2

ρσ
σ
σ
2
 1 2

1.
2.
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if ρ = 0 then X1 and X2 are independent ;
given a1,a2∈IR, a1X1+a2X2 is normal .
44
Example
Given (X1,X2) a normal bivariate random vector
with mean vector (50,45) and covariance matrix
 6 2

Σ = 
 2 4
Determine:
a) P(4X1+X2 ≥ 250)
b) P(X1+4X2 ≥ 220)
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45

Distribution models

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