Generating Random Variables(1)

Computational Statistics Ch 3 Methods for generating random variables Prof. Donna Pauler Ankerst

Book Statistical Computing with R Maria L. Rizzo Chapman & Hall/CRC, 2008 Please review yourself: Chapter 1: Introduction to the R environment Chapter 2: Probability and statistics review

Random variable simulation

 The fundamental tool required in compuational statistics is the ability to simulate random variables from specified probability distributions.

 All random variable generation starts with uniform random variable generation.

 A uniform distribution means all values in the domain space of consideration have equal probability of occurrence.

Discrete uniform

 Let the random variable Y be the outcome from the roll of a single fair die. What is the distribution of Y?

Answer: Y ~ Discrete Uniform on {1,2,3,4,5,6} where p(Y = i) = 1/6 for i = 1,...,6.  Write R code to simulate a random sample of 600 observations of Y and show a histogram to prove it follows the correct distribution.

Answer: >ysamp=sample(1:6,600,replace=T)

>hist(ysamp)

Discrete uniform >ysamp=sample(1:6,600,replace=T) >hist(ysamp)

I expect the bars to be of the same height, 100 of each, so I try again.

This must be random variation, double check by increasing from 600 to 60000.

Discrete uniform >ysamp=sample(1:6,60000,replace=T) >hist(ysamp)

That is better, now I am convinced the sample command is doing what I think it is doing.

More about the R sample function

The multinomial distribution is not uniform in general, it allows different values to have different probabilities as long as they sum to one.

Continuous uniform

>ysamp=runif(1000) >hist(ysamp,prob=T)

The height is near 1, correct for the U(0,1) density.

 Let the random variable Y come from the continuous uniform distribution on the interval (0,1) [U(0,1) density]. Write down the density function of Y.

Answer: Y ~ f(Y) where f(Y) = 1 for 0 < Y < 1, and 0 otherwise.  Write R code to simulate a random sample of 1000 observations of Y and show an empirical density plot to prove it follows the correct distribution.

Answer:

Uniform(0,1)

 The U(0,1) distribution provides the base generating point for generating most distributions.

 Most programming languages, such as C, and statistical packages, such as R, include a U(0,1) generator.

 There are many computer algorithms for generating U(0,1) random variables based on congruential methods. These fall more in the real of informatics and are beyond the scope of this course.

 For this course we will assume that we have a method for generating a U(0,1) random variable.

Uniform(a,b)

>hist(1+3*runif(1000),prob=T)

 Question: How would you generate Z ~ U(a,b), the uniform distribution on (a,b), if you only had a U(0,1) generator available?  Answer: Generate Y ~ U(0,1) and let Z = a + (b-a)Y.

Write the R code to verify this for a = 1, b = 4.

The height of a U(a,b) density is 1/(b-a) and the height appears to be near 1/3 as expected for U(1,4).

Generators in R

In general, p returns the cdf and d the pdf evaluated at a given value, q returns a quantile and r returns a random number from the distribution. Try these functions out yourself. Use the help command, eg help(runif).

Discrete random variables

Although R now contains generators for most discrete distributions, and the list is constantly growing, we will learn the algorithms behind them. Specifically, we will now cover how to generate from the following distributions  Bernoulli  Binomial  Discrete

assuming that a U(0,1) generator is available.

Bernoulli(p)

(0,1).~for 1)(0)()(1)(

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)( 2.))1,0(~ Generate 1.)

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,...,nipBerpUI

,...,niUU

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nk-ppkn

kXPpnBinX

=⎟⎟⎠

⎞⎜⎜⎝

You may have forgotten your probability distributions. These are reviewed in Ch. 2 of the Rizzo book or can be found in Wikipedia. It is important to know the distributions well to be able to simulate from them. The Binomial distribution counts the number of successes in n independent Bernoulli trials.

Discrete(x1,x2,...,xn)

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,...,njpFF

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REMARK This algorithm can be extended for a very large and even infinitely countable n, but the search for the correct interval can become numerically infeasible. For these cases binary and indexed searches can be used (Ripley 1987).

Inverse Transform Method

 To move on to generating from common continuous distributions or infinite discrete distributions we need a new general and useful technique.

 The inverse transform method works for any random variable that has an invertible cdf.

Theorem: Probability Integral Transformation

.))(())((

))())((())(()(

uniform. a of cdf thefollows )( thatshow We.)P( satisfies variable

1)uniform(0, a of cdf the10For :Proof

).1,0(~)(then ),( cdf with variablerandom continuous a is if note,First

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XFUuuUU

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==≤=

≤=≤=≤

≤≤

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X ≤=

Theorem: Probability Integral Transformation

on.distributi (0,1) theof cdf theof definitionby )())(())())((())(()( :Proof

).( 2.)(0,1).~ Generate .)1

:)( pdf hence and ,)( cdf with X generate Then to

.(0,1)for })(:inf{)(ation transforminverse theDefine

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uuxFxuF

≤=≤=≤

∈==−

Example from book

Good fit!

Example: Exponential distribution

The exponential distribution is often used in industrial research to model time until failure of machines, light bulbs, etc.

Example: Exponential distribution

.)1(1)1P(1)1P()1( :Proof(0,1).both are 1 and since )/log( setting toequivalent is This

)/1log()1log(

)exp(1)(exp1

.for )( Solve)(Set (0,1).~ Generate

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UXλXUλXUUλX

XUXFUFXUU

=−−=−<−=≤−=≤−

−−=

−=−

=−−

To generate n Exp(lambda) random variables in R: >-log(runif(n))/lambda R has an exponential generator >rexp(n,lambda)

Inverse Transformation Method, Discrete Case

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xXxFUxFx

xFuxFxuFxF

≤<•

Set )()( where Find

)10( is algorithm The

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Example: Poisson distribution

.Set )()1( where Find

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,algorithmsefficient more are hereAlthough t

xXxFUxFx

xxfxfxfxFxF

ffeeffF

efF-λ

≤<−•

+=++−=

+=+=+=

To generate n Poisson(lambda) random variables in R: >rpois(n,lambda)

Acceptance-Rejection Algorithm

You want to generate from some distribution f(y) but just do not know how.

What you need is a density g(y) that you can sample from and such that cg(y) covers f(y) in the sense that cg(y) ≥ f(y) for all y such that f(y) > 0.

(a). back to go otherwise

;return and accept )()( If (c)

(0,1)~ Generate (b))(~ Generate (a)

required variablerandomeach For

from. samplecan you that and

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tftctgtfYg

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(0,1)~ Generate (b))(~ Generate (a)

required variablerandomeach For

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UUaaUPYcgYf

YYcgYfUPYP

YXYYcgYfU

≤≤

⎟⎟⎠

⎞⎜⎜⎝

⎛≤=

So the closer cg(Y) is to f(Y) the higher the acceptance probability and the more efficient the algorithm.

The closer cg(Y) is to f(Y) the higher the acceptance probability and the more efficient the algorithm. It can be a challenge to find an efficient g(Y). High probabilities

of rejection regions

Adaptive rejection, implemented in the R Winbugs package, uses g(Y) as a piecewise spline approximation to densities f(Y) that are log concave [Not covered in detail here].

g(y) Sometimes g(Y) is called the envelope density.

.])1(11mean has that variable,random geometric a istry eachon 1y probabilit with success a until iterations ofnumber [The

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rejection acceptance by the generated r.v. that theProof

XYXfYfc

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The first step to successfully using acceptance-rejection is knowing what your density f(y) looks like. For multivariate densities this can be very difficult.

Example: Beta distribution

Beta(1,1) = U(0,1)

.)1()(

.1!0,)!1()( 1, numbers naturalFor

0. 0, 1,0for )1()()()()(),Beta(~

Beta(2,2).~ nsobservatio 1,000 generate toalgorithmrejection -acceptance theUse

=−=Γ≥

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+Γ=⇒

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βα βα

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Example: Beta distribution

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−=−=−ΓΓ

−−

cUxgxf

xxxxxxxf

Beta(2,2) is symmetric and unimodal in [0,1].

The book hastily chooses the same U(0,1) generating density but c = 6 instead of 3/2. It therefore requires 4 times the iterations.

R Code for Example 3.7

5873 iterations required to get

1000 r.v.’s

Missing a square as shown next. The correct se yields similar conclusions.

Errors in the book

Ch 2, not Ch 1. Show next.

Variance of the qth sample quantile

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> u = rgamma(1000,shape=3,rate=1) > v = rgamma(1000,shape=2,rate=1) > qqplot(qbeta(ppoints(1000),3,2),u/(u+v)) > abline(0,1)

ppoints(): Generates a symmetric series in [0,1]. You could have used seq(0,1,length=1000) instead to get evenly spaced numbers in [0,1] or the rbeta(1000,3,2) to just get a random sample.

Example 3.8

Transformations

−−

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xppxXPpGeomX

xppxrx

xXPprNegBinX

prNegBin

Niidvv

=−==⇒

=−⎟⎟⎠

⎞⎜⎜⎝

⎛ −+==⇒

There are now R functions for these (rchisq,rnbinom, rgamma) but this is not just an R course.

Mixtures

.1)(such that function weighting

and numbers realby indexed )( onsdistributi offamily afor

)()( )(

is ofon distributi theif mixture continuous a follows variablerandomA

ies.probabilit mixingor

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dyyfxFxF

xFπxFXX

iXiX i

Example: Mixture of Normals

To generate a random variable that follows a 0.5:0.5 mixture of independent N(0,1) and N(3,1) distributions: 1.) Generate an integer k in {1,2}, where P(1) = P(2) = 0.5. 2.) If k = 1, draw X ~ N(0,1); otherwise X ~ N(3,1).

A mixture of Normals is different from a convolution of Normals. A mixture of Normals may not be Normal, it may be bimodal or multimodal. Is a convolution of Normals Normal?

Example: Convolution of Normals

What is the distribution of the convolution of independent N(0,1) and N(3,1) distributions? Let X = X1 + X2, where X1 ~ N(0,1) and X2 ~ N(3,1) Then from Statistics courses we know that   E(X) = E(X1) + E(X2) = 0 + 3 = 3 ...linearity of expectation   Var(X) = Var(X1) + Var(X2) = 1 + 1 = 2 ...independence   X ~ Normal ...characteristic functions

Therefore, X ~ N(3,2).

Example: Mixture of Gammas

densities.component over the mixture theof estimatedensity empiricalan overlay and , from nsobservatio 5000 simulate toloops

withoutcode Refficient Write1,...,5.for ,15/ are iesprobabilit

mixing theandt independen are )/1,3( where

:onsdistributi Gamma of mixture following heConsider t5

FjjπjrGamma~X

WAKE-UP CALL: A question like this could appear on the exam!

Example: Mixture of Gammas

ghts.higher weiget meanshigher with onsdistributi theSo 5/15. 4/15, 3/15, 2/15, 1/15,

ies,probabilit mixing respective with 53,6,9,12,1 are means The

1,...,5.for 3/)( on,distributi for the Recall...simulating areyou what know Get to

1,...,5.for ,15/ iesprobabilit

mixing with )/1,3( where,5

jjrXEGamma

jrGamma~XFπF

===∑=

Ex. 3.12 R code

density(): Generates a kernel density estimate, like a smoothed histogram, for a sample of data.

Your code would not have to appear exactly like this but would need to work and not contain loops.

Ex. 3.12 Mixture of several gamma distributions

Ex. 3.15 Poisson-gamma mixture

)).1/(,(~

),(~ ),(~X

ly,Specifical ).,(~ whereons,distributi )( of mixture continuous a also is success)th theuntil

failures ofnumber the(countson distributi Binomial Negative The

βλλ

rNegBinX

rGammaPoisson

rGammaPoissonr

Question: Simulate 5000 observations of a variable that counts the number of failures until the 4th success, where the probability of success on each independent trial is 0.75 using a continuous mixture.

Question: Simulate 5000 observations of a variable that counts the number of failures until the 4th success, where the probability of success on each independent trial is 0.75 using a continuous mixture. Solution: From the description we recognize this as a NegBin random variable. By the continuous mixture requirement we, see that we need to use the Poisson-Gamma method.

3. and 4 need))1/(,(~),(~ ),(~X

βββλλ

rrNegBinXrGammaPoisson

Histogram of x

Frequency

0 2 4 6 8

> hist(x) Check that the simulations make sense: if X counts the number of failures until the 4th success where each success probability is 0.75, expect lots of 0’s.

Ex. Poisson-gamma mixture

R code and output comparing the simulation probability mass function with a NegBin(4,.75) mass function:

The Empirical Cumulative Distribution Function (ecdf, Ch 2 of book) is a good method for comparing distributions.

.5.0/)](1)[()( oferror standard The

.1,1,...,1,/

:by ... sample ordered observedan for defined and)()( of estimate unbiasedan is )( ecdf The

)()2((1)

nnxFxFxF

xxnixxxni

xxxxX P xFxF

≤−=

⎪⎩

⎪⎨

−=<≤

≤≤≤

Comparing distributions

nnxFxFxFn

5.0/)](1)[()]([ s.e. ≤−=

R code and output comparing the ecdf of the simulated sample with the probability mass function of a NegBin(4,.75):

Multivariate Normal distribution

( ) .)(Σ)(21expΣ2)(

function density hasit if ),Σ,(~ denoted ,matrix variance

symmetric definite positive and r mean vectoon with distributi

Normal temultivaria ldimensiona- a has vector randomA

12/12/

⎭⎬⎫

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⎟⎟⎟

⎜⎜⎜

⎟⎟⎟

⎜⎜⎜

∈⎟⎟⎟

⎜⎜⎜

−−− µµπ

Covered in Multivariate Statistics

determinant inverse

The R package already contains efficient functions to generate multivariate normal random variables: •  mvrnorm in the MASS package •  rmvnorm in the mvtnorm package. However, it is useful to see how these are traditionally generated from univariate N(0,1) random variables.

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dqZYRb

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ʹ′=ʹ′==+=

+=+=+=+=

+=Σ ×

Multivariate Normal distribution Suppose that Σ can be factorized as Σ =C "C . Then if Z~Nd (0,I ) and Y =CZ +µ,

E(Y ) = E(CZ +µ) = E(CZ )+E(µ) =CE(Z )+µ = µVar(Y ) =Var(CZ +µ) =Var(CZ ) =CVar(Z ) "C =C "C = Σ.

and Y is also Normal on d dimensions. This gives the algorithm for generating the random variable, but how is the decomposition of Σ performed?

Three methods in R: •  eigen: spectral or eigenvalue decomposition •  chol: Cholesky factorization •  svd: Singular value decomposition

Defintions and examples of how to use these from the book

Three methods in R: •  eigen: spectral or eigenvalue decomposition •  chol: Cholesky factorization •  svd: Singular value decomposition

You will not need to memorize the specific decompositions for the exam, they would be provided and you would have to write R code.

Ex. 3.16 Spectral decomposition method

Compare simulated means, covariances to the truth (here covariances = correlations since the variances = 1).

Ex. 3.17 Singular value decomposition method

Ex. 3.18 Choleski factorization method

Wishart distribution

mean. sample theis 1 where

then),(~,..., if that learning recallmight You

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nW~MnMdnN

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Bartlett’s Decomposition (not required to memorize for the exam)

).,Σ(~Then 2.). and 1.)by Generate

ngular.lower tria is where,Σion factorizat Choleski Obtain the

:),Σ( a generate To

).,(~Then

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satisfying

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nWLLAA

nIWTTA

ʹ′•

ʹ′=•

ʹ′=

+−χ

End of Chapter 3

Chapter 4: Visualization of Multivariate Data Has some interesting and straightforward concepts but the R packages used in the chapter are now outdated. There are now many advanced R packages for plotting data, including ggplot. Chapter 4 is not covered in this course.

Generating Random Variables(1)

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