stat341f11

Sampling - Sept 20, 2011

The meaning of sampling is to generate data points or numbers such that these data follow a certain distribution.
i.e. From [math]\displaystyle{ x \sim~f(x) }[/math] sample [math]\displaystyle{ \,x_{1}, x_{2}, ..., x_{1000} }[/math]

In practice, it maybe difficult to find the joint distribution of random variables. Through simulating the random variables, we can inference from the data.

Sample from uniform distribution.

Computers can't generate random numbers as they are deterministic, but they can produce pseudo random numbers. Pseudo random numbers are numbers that mimic the properties of random numbers, but are generated using algorithms. Therefore, they are not truly random.

Multiplicative Congruential

• involves three parameters: integers [math]\displaystyle{ \,a, b, m }[/math], and an initial value [math]\displaystyle{ \,x_0 }[/math] we call the seed
• a sequence of integers is defined as

[math]\displaystyle{ x_{k+1} \equiv (ax_{k} + b) \mod{m} }[/math]

Example: [math]\displaystyle{ \,a=13, b=0, m=31, x_0=1 }[/math] creates a uniform histogram.

MATLAB code for generating 1000 random numbers using the multiplicative congruential method:

a=13;
b=0;
m=31;
x(1)=1;

for ii = 1:1000
    x(ii+1) = mod(a*x(ii)+b,m);
end

MATLAB code for displaying the values of x generated:

x

MATLAB code for plotting the histogram of x:

hist(x)

Facts about this algorithm:

• In this example, the first 30 terms in the sequence are a permutation of integers from 1 to 30 and then the sequence repeats itself.
• Values are between 0 and [math]\displaystyle{ m-1 }[/math], inclusive.
• Dividing the numbers by [math]\displaystyle{ m-1 }[/math] yields numbers in the interval [math]\displaystyle{ [0,1] }[/math].
• MATLAB's rand function once used this algorithm with [math]\displaystyle{ a = 7^5, b = 0, m = 2^{31}-1 }[/math], for reasons described in Park and Miller's 1988 paper "Random Number Generators: Good Ones are Hard to Find" (available online).
• Visual Basic's RND function also used this algorithm with [math]\displaystyle{ a = 1140671485, b = 12820163, m = 2^{24} }[/math] (Reference)

Inverse Transform Method

• We want to sample from distribution [math]\displaystyle{ X }[/math] with probability density function (pdf) [math]\displaystyle{ f(x) }[/math]
• Assume that the cumulative distribution function (cdf) [math]\displaystyle{ F(x) }[/math] and its inverse [math]\displaystyle{ F^{-1}(x) }[/math] can be found.

[math]\displaystyle{ P(a \leq x\lt b)=\int_a^{b} f(x) dx }[/math]

[math]\displaystyle{ cdf=F(x)=P(X \leq x)=\int_{-\infty}^{x} f(x) dx }[/math]

Theorem:

Take [math]\displaystyle{ U \sim~ \mathrm{Unif}[0, 1] }[/math] and let [math]\displaystyle{ x=F^{-1}(u) }[/math]. Then x has distribution function [math]\displaystyle{ F(\cdot) }[/math], where [math]\displaystyle{ F(x)=P(X \leq x) }[/math].

Let [math]\displaystyle{ F^{-1}() }[/math] denote the inverse of [math]\displaystyle{ F() }[/math]. Therefore [math]\displaystyle{ F(x)=u \implies x=F^{-1}(u) }[/math]

Take the exponential distribution for example

[math]\displaystyle{ \,f(x)={\lambda}e^{-{\lambda}x} }[/math]

[math]\displaystyle{ \,F(x)=\int_0^x {\lambda}e^{-{\lambda}u} du }[/math]

[math]\displaystyle{ \,F(x)=1-e^{-{\lambda}x} }[/math]

Let: [math]\displaystyle{ \,F(x)=y }[/math]

[math]\displaystyle{ \,y=1-e^{-{\lambda}x} }[/math]

[math]\displaystyle{ \,ln(1-y)={-{\lambda}x} }[/math]

[math]\displaystyle{ \,x=\frac{ln(1-y)}{-\lambda} }[/math]

[math]\displaystyle{ \,F^{-1}(x)=\frac{-ln(1-x)}{\lambda} }[/math]

Therefore, to get a exponential distribution from a uniform distribution takes 2 steps.

• Step 1. Draw [math]\displaystyle{ U \sim~ \mathrm{Unif}[0, 1] }[/math]
• Step 2. [math]\displaystyle{ x=\frac{-ln(1-U)}{\lambda} }[/math]

MATLAB code for exponential distribution case,assuming [math]\displaystyle{ \lambda=0.5 }[/math]

for ii = 1:1000
    u = rand;
    x(ii)=-log(1-u)/0.5;
    ii+1;
end
hist(x)

MATLAB result

Now we just have to show the generated points have a cdf of [math]\displaystyle{ F(x) }[/math]

[math]\displaystyle{ \,P(F^{-1}(u) \leq x) }[/math]

[math]\displaystyle{ \,P(F(F^{-1}(u)) \leq F(x)) }[/math]

[math]\displaystyle{ \,P(u \leq F(x)) }[/math]

[math]\displaystyle{ \,=F(x) }[/math]

QED

Discrete Case

This same technique can be applied to the discrete case. Generate a discrete random variable [math]\displaystyle{ \,x }[/math] that has probability mass function [math]\displaystyle{ \,P(X=x_i)=P_i }[/math] where [math]\displaystyle{ \,x_0\lt x_1\lt x_2... }[/math] and [math]\displaystyle{ \,\sum_i P_i=1 }[/math]

• Step 1. Draw [math]\displaystyle{ u \sim~ \mathrm{Unif}[0, 1] }[/math]
• Step 2. [math]\displaystyle{ \,x=x_i }[/math] if [math]\displaystyle{ \,F(x_{i-1})\lt u \leq F(x_i) }[/math]

Inverse Transform Method and Acceptance/Rejection Method - Sept 22, 2011

Discrete Case (continued)

Let x be a discrete random variable with the following probability mass function:

[math]\displaystyle{ \begin{align} P(X=0) = 0.3 \\ P(X=1) = 0.2 \\ P(X=2) = 0.5 \end{align} }[/math]

Given the pmf, we now need to find the cdf.

We have:

[math]\displaystyle{ F(x) = \begin{cases} 0 & x \lt 0 \\ 0.3 & 0 \leq x \lt 1 \\ 0.5 & 1 \leq x \lt 2 \\ 1 & 2 \leq x \end{cases} }[/math]

We can apply the inverse transform method to obtain our random numbers from this distribution.

Pseudo Code for generating the random numbers:

Draw U ~ Unif[0,1] 
     if U <= 0.3 
        x = 0 
     else if  
        0.3 < U <= 0.5 
        x = 1 
     else 
        0.5 < U <= 1 
        x = 2

MATLAB code for generating 1000 random numbers in the discrete case:

for ii = 1:1000
    u = rand;
    
    if u <= 0.3
       x(ii) = 0;
       else if u <= 0.5
           x(ii) = 1;
           else
             x(ii) = 2;
    end
end

Pseudo code for the Discrete Case:

1. Draw U ~ Unif [0,1]

2. If [math]\displaystyle{ U \leq P_0 }[/math], deliver [math]\displaystyle{ X = x_0 }[/math]

3. Else if [math]\displaystyle{ U \leq P_0 + P_1 }[/math], deliver [math]\displaystyle{ X = x_1 }[/math]

4. Else If [math]\displaystyle{ U \leq P_0 +....+ P_k }[/math], deliver [math]\displaystyle{ X = x_k }[/math]

Although this method is useful, it isn't practical in many cases since we can't always obtain [math]\displaystyle{ F }[/math] or [math]\displaystyle{ F^{-1} }[/math] as some functions are not integrable or invertible, and sometimes even [math]\displaystyle{ f(x) }[/math] itself cannot be obtained in closed form. Let's look at some examples:

• Continuous case

If we want to use this method to draw the pdf of normal distribution, we may find ourselves get stuck in finding its cdf. The simplest case of normal distribution is [math]\displaystyle{ f(x)=\frac{1}{\sqrt{2\pi}}e^{-\frac{x^2}{2}} }[/math], whose cdf is [math]\displaystyle{ F(x)=\frac{1}{\sqrt{2\pi}}\int_{-\infty}^{x}{e^{-\frac{u^2}{2}}}du }[/math]. This integral cannot be expressed in terms of elementary functions. So evaluating it and then finding the inverse is a very difficult task.

• Discrete case

It is easy for us to simulate when there are only a few values taken by the particular random variable, like the case above. And it is easy to simulate the binomial distribution [math]\displaystyle{ X \sim~ \mathrm{B}(n,p) }[/math] when the parameter n is not too large. But when n takes on values that are very large, say 50, it is hard to do so.

Acceptance/Rejection Sampling

The aforementioned difficulties of the inverse transform method motivates a sampling method that does not require analytically calculating cdf's and their inverses, which is the acceptance/rejection sampling method. Here, [math]\displaystyle{ f(x) }[/math] is approximated by another function, say [math]\displaystyle{ g(x) }[/math], with the idea being that [math]\displaystyle{ g(x) }[/math] is a "nicer" function to work with than [math]\displaystyle{ f(x) }[/math].

Suppose we assume the following:

1. There exists another distribution [math]\displaystyle{ g(x) }[/math] that is easier to work with and that you know how to sample from, and

2. There exists a constant c such that [math]\displaystyle{ f(x) \leq c \cdot g(x) }[/math] for all x

Under these assumptions, we can sample from [math]\displaystyle{ f(x) }[/math] by sampling from [math]\displaystyle{ g(x) }[/math]

General Idea

Looking at the image below we have graphed [math]\displaystyle{ c \cdot g(x) }[/math] and [math]\displaystyle{ f(x) }[/math].

Using the acceptance/rejection method we will accept some of the points from [math]\displaystyle{ g(x) }[/math] and reject some of the points from [math]\displaystyle{ g(x) }[/math]. The points that will be accepted from [math]\displaystyle{ g(x) }[/math] will have a distribution similar to [math]\displaystyle{ f(x) }[/math]. We can see from the image that the values around [math]\displaystyle{ x_1 }[/math] will be sampled more often under [math]\displaystyle{ c \cdot g(x) }[/math] than under [math]\displaystyle{ f(x) }[/math], so we will have to reject more samples taken at x₁. Around [math]\displaystyle{ x_2 }[/math] the number of samples that are drawn and the number of samples we need are much closer, so we accept more samples that we get at [math]\displaystyle{ x_2 }[/math]

Procedure

1. Draw y ~ g

2. Draw U ~ Unif [0,1]

3. If [math]\displaystyle{ U \leq \frac{f(y)}{c \cdot g(y)} }[/math] then x=y; else return to 1

Example

Sample from Beta(2,1)

In general:

Beta([math]\displaystyle{ \alpha, \beta) = \frac{\Gamma (\alpha + \beta)}{\Gamma(\alpha)\Gamma(\beta)} }[/math] [math]\displaystyle{ x^{\alpha-1} }[/math] [math]\displaystyle{ (1-x)^{\beta-1} }[/math], [math]\displaystyle{ 0\lt x\lt 1 }[/math]

Note: [math]\displaystyle{ \Gamma(n) = (n-1)! }[/math] if n is a positive integer

[math]\displaystyle{ \begin{align} f(x) &= Beta(2,1) \\ &= \frac{\Gamma(3)}{\Gamma(2)\Gamma(1)} x^1(1-x)^0 \\ &= \frac{2!}{1! 0!}\cdot (1) x \\ &= 2x \end{align} }[/math]

We want to choose [math]\displaystyle{ g(x) }[/math] that is easy to sample from. So we choose [math]\displaystyle{ g(x) }[/math] to be uniform distribution.

We now want a constant c such that [math]\displaystyle{ f(x) \leq c \cdot g(x) }[/math] for all x from (0,1)

So,

[math]\displaystyle{ c \geq \frac{f(x)}{g(x)} }[/math], for all x from (0,1)

[math]\displaystyle{ \begin{align}c &\geq max (\frac {f(x)}{g(x)}, 0\lt x\lt 1) \\ &= max (\frac {2x}{1},0\lt x\lt 1) \\ &= 2 \end{align} }[/math]

Now that we have c =2,

1. Draw y ~ g(x) => Draw y ~ Unif [0,1]

2. Draw u ~ Unif [0,1]

3. if [math]\displaystyle{ u \leq \frac{2y}{2 \cdot 1} }[/math] then x=y; else return to 1

MATLAB code for generating 1000 samples following Beta(2,1):

close all
clear all
ii=1;
while ii < 1000
    y = rand;
    u = rand;
    
    if u <= y
        x(ii)=y;
        ii=ii+1;
    end
end
hist(x)

MATLAB result

Limitations

Most of the time we have to sample many more points from g(x) before we can obtain an acceptable amount of samples from f(x), hence this method may not be computationally efficient. It depends on our choice of g(x). For example, in the example above to sample from Beta(2,1), we need roughly 2000 samples from g(X) to get 1000 acceptable samples of f(x).

Proof

Mathematically, we need to show that the sample points given that they are accepted have a distribution of f(x).

[math]\displaystyle{ \begin{align} P(y|accepted) &= \frac{P(y, accepted)}{P(accepted)} \\ &= \frac{P(accepted|y) P(y)}{P(accepted)}\end{align} }[/math] (Bayes' Rule)

[math]\displaystyle{ P(y) = g(y) }[/math]

[math]\displaystyle{ P(accepted|y) =P(u\leq \frac{f(y)}{c \cdot g(y)}) =\frac{f(y)}{c \cdot g(y)} }[/math],where u ~ Unif [0,1]

[math]\displaystyle{ P(accepted) = \sum P(accepeted|y)\cdot P(y)=\int^{}_y \frac{f(y)}{c \cdot g(y)}g(y) dy=\int^{}_y \frac{f(y)}{c} dy=\frac{1}{c} \cdot\int^{}_y f(y) dy=\frac{1}{c} }[/math]

So,

[math]\displaystyle{ P(y|accepted) = \frac{ \frac {f(y)}{c \cdot g(y)} \cdot g(y)}{\frac{1}{c}} =f(y) }[/math]

Discrete Example

Generate random variables according to the p.m.f:

[math]\displaystyle{ \begin{align} P(Y=1) = 0.15 \\ P(Y=2) = 0.22 \\ P(Y=3) = 0.33 \\ P(Y=4) = 0.10 \\ P(Y=5) = 0.20 \end{align} }[/math]

find a g(y) discrete uniform distribution from 1 to 5

[math]\displaystyle{ c \geq \frac{P(y)}{g(y)} }[/math]
[math]\displaystyle{ c = \max \left(\frac{P(y)}{g(y)} \right) }[/math]
[math]\displaystyle{ c = \max \left(\frac{0.33}{0.2} \right) = 1.65 }[/math] Since P(Y=3) is the max of P(Y) and g(y) = 0.2 for all y.

1. Generate Y according to the discrete uniform between 1 - 5

2. U ~ unif[0,1]

3. If [math]\displaystyle{ U \leq \frac{P(y)}{1.65 \times 0.2} \leq \frac{P(y)}{0.33} }[/math], then x = y; else return to 1.

In MATLAB, the code would be:

   py = [0.15 0.22 0.33 0.1 0.2];
   ii =1;
   while ii <= 1000
       y = unidrnd(5);
       u = rand;
       if u <= py(y)/0.33
           x(ii) = y;
           ii = ii+1;
       end
   end
   hist(x);

MATLAB result

Sampling From Gamma and Normal Distribution - Sept 27, 2011

Sampling From Gamma

The Gamma function is written as [math]\displaystyle{ X \sim~ Gamma (t, \lambda) }[/math]

[math]\displaystyle{ F(x) = \int_{0}^{\lambda x} \frac{e^{-y}y^{t-1}}{(t-1)!} dy }[/math]

If you have t samples of the exponential distribution,

[math]\displaystyle{ \begin{align} x_1 \sim~ Exp(\lambda)\\ \vdots \\ x_t \sim~ Exp(\lambda) \end{align} }[/math]

The sum of these t samples has a gamma distribution,

[math]\displaystyle{ x_1+x_2+ ... + x_t \sim~ Gamma (t, \lambda) }[/math]
[math]\displaystyle{ \sum_{i=1}^{t} x_i \sim~ Gamma (t, \lambda) }[/math]

We can sample the exponential distribution using the inverse transform method from previous class,
[math]\displaystyle{ \,f(x)={\lambda}e^{-{\lambda}x} }[/math]
[math]\displaystyle{ \,F^{-1}(u)=\frac{-ln(1-u)}{\lambda} }[/math]
[math]\displaystyle{ \,F^{-1}(u)=\frac{-ln(u)}{\lambda} }[/math] 1 - u is the same as x since [math]\displaystyle{ U \sim~ unif [0,1] }[/math]
[math]\displaystyle{ \begin{align} \frac{-ln(u_1)}{\lambda} - \frac{ln(u_2)}{\lambda} - ... - \frac{ln(u_t)}{\lambda} = x_1\\ \vdots \\ \frac{-ln(u_1)}{\lambda} - \frac{ln(u_2)}{\lambda} - ... - \frac{ln(u_t)}{\lambda} = x_t \end{align} }[/math]
[math]\displaystyle{ \frac {-\sum_{i=1}^{t} ln(u_i)}{\lambda} = x }[/math]

MATLAB code for a Gamma(1,3) is

x = sum(-log(rand(1000,3)),2); 
hist(x)

And the Histogram of X follows a Gamma distribution with long tail:

The R code for a Gamma(1,3) is

a<-apply(-log(matrix(runif(3000),nrow=1000)),1,sum);
hist(a);

And the histogram is

File:hist gamma.png

Here is another histogram of Gamma coding with R

a<-apply(-log(matrix(runif(3000),nrow=1000)),1,sum);
hist(a,freq=F);
lines(density(a),col="blue");
rug(jitter(a));

File:hist gamma 2.png

Sampling From Normal Distribution

We will focus on the standard normal distribution [math]\displaystyle{ Z \sim~ N (0, 1) }[/math]

[math]\displaystyle{ F(Z) = \int_{-\infty}^{Z}\frac{1}{\sqrt{2\pi}}e^{-x^2/2}dx }[/math]

Note: Any points on the cartesian plane can be written in two forms:

1. Rectangular Coordinates: [math]\displaystyle{ z = (x , y) }[/math]
2. Polar Coordinates: [math]\displaystyle{ z = (R , \theta) }[/math]

Suppose X and Y are two standard normals and that they are independent, then the joint distribution function of X and Y is

[math]\displaystyle{ f(X, Y) = f(X) f(Y) = \frac{1}{\sqrt{2\pi}}e^{-x^2/2} \frac{1}{\sqrt{2\pi}}e^{-y^2/2} = \frac{1}{2\pi}e^{-(x^2+y^2)/2} }[/math]

Notice that [math]\displaystyle{ x^2+y^2 = R^2 }[/math] and [math]\displaystyle{ \theta = tan^{-1}(\frac{y}{x}) }[/math], so [math]\displaystyle{ f(X, Y) }[/math] can be written as [math]\displaystyle{ f(R, \theta) }[/math]

[math]\displaystyle{ f(R, \theta)= \frac{1}{2\pi}e^{-R^2/2} }[/math]

The first part [math]\displaystyle{ \frac{1}{2\pi} }[/math] is an uniform distribution. The second part [math]\displaystyle{ e^{-R^2/2} }[/math] is a exponential distribution with [math]\displaystyle{ \lambda = 1/2 }[/math].

Sampling From Normal Distributions with Co-Varience - Sept 29, 2011

Box-Muller Transform

The Box-Muller Transform allows us to sample values from a standard normal distribution, providing X ~ Norm(0, 1) and Y ~ Norm(0, 1).

Method

1. Generate [math]\displaystyle{ u_1 }[/math] and [math]\displaystyle{ u_1 }[/math], two values sampled from a uniform distribution between 0 and 1.

2.

   [math]\displaystyle{ R^2 = -2log(u_1) }[/math] where [math]\displaystyle{  R^2 }[/math] is exponential with mean 1/2

   [math]\displaystyle{ \theta = 2*\pi*u_2 }[/math] where [math]\displaystyle{  \theta \sim~ \mathrm{Unif}[0, 2\pi] }[/math]

3.

   [math]\displaystyle{ X = R cos(\theta) }[/math]

   [math]\displaystyle{ Y = R sin(\theta) }[/math]

Justification

Suppose we have X ~ N(0, 1) and Y ~ N(0, 1). The probability density function of these two random variables using Cartesian coordinates is:

[math]\displaystyle{ f(x, y) = ... }[/math]

In polar coordinates [math]\displaystyle{ R^2 = x^2 + y^2 }[/math], so the probability density function of these two random variables using polar coordinates is:

[math]\displaystyle{ f(x, y) = ... }[/math]

Example

// In progress

Non-Standard Normal Distributions

// In progress

Normal Distributions With Co-Varience

// In progress

Sampling From Binomial Distributions

// In progress