Ch. 14: Markov Chain Monte Carlo Methods based on Stephen Marsland, Machine Learning: An Algorithmic Perspective. CRC 2009.; C, Andrieu, N, de Freitas,

Ch. 14: Markov Chain Monte Carlo Methods

based on Stephen Marsland, Machine Learning: An Algorithmic Perspective. CRC 2009.;C, Andrieu, N, de Freitas, A, Doucet, M, I. Jordan. An Introduction to MCMC for Machine Learning. Machine Learning, Vol. 50, No. 1. pp. 5-43, 2003,and Wikipedia

Longin Jan LateckiTemple University

latecki@temple.edu

Random Numbers

• Pseudo-random numbers from uniform distribution:xn+1 = (axn + c) mod m,a, c, m are parameters,e.g., m=232, a=1,664,525, and c=1,013,904,223

Monte Carlo Method

While convalescing from an illness in 1946, Stanislaw Ulam was playing solitaire. It, then, occurred to him to try to compute the chances that a particular solitaire laid out with 52 cards would come out successfully (Eckhard, 1987). After attempting exhaustive combinatorial calculations, he decided to go for the more practical approach of laying out several solitaires at random and then observing and counting the number of successful plays. This idea of selecting a statistical sample to approximate a hard combinatorial problem by a much simpler problem is at the heart of modern Monte Carlo simulation. Stanislaw Ulam soon realized that computers could be used in this fashion to answer questionsof neutron diffusion and mathematical physics.

From An Introduction to MCMC for Machine Learning by: C, Andrieu, N, de Freitas, A, Doucet, M, I. JordanMachine Learning, Vol. 50, No. 1. pp. 5-43, 2003.

Random Samples and Statistical Models

• Random Sample: A random sample is a collection of random variables X1, X2,…, XN, that that have the same probability distribution and are mutually independent– If F is a distribution function of each random

variable Xi in a random sample, we speak of a random sample from F. Similarly we speak of a random sample from a density f, e.g., a random sample from an N(µ, σ2) distribution, etc.

• Statistical Model for repeated measurementsA dataset consisting of values x(1), x(2),…, x(N) of

repeated measurements of the same quantity is modeled as the realization of a random sample

X1, X2,…, XN.

The model may include a partial specification of the probability distribution of each Xi.

Distribution features and sample statistics

• Empirical Distribution Function

• Law of Large Numbers– lim N->∞ P(|FN(a) – F(a)| > ε) = 0

• This implies that for most realizations– FN(a) ≈ F(a)

N

axaF

i

N

]),((#)(

)(

• The histogram

i.e., the height of histogram on (x-h, x+h] ≈ f(x)We recall that the probability density function (pdf) f of a

random variable X is a derivative of the cumulative distribution function (cdf) F(t)=Pr(X<t):

In particular, we have

N

hxhxxxf

i ]),((#)(

)(

x

duufxF )()()()( xFdx

dxf

x

duufxd

dxF

xd

dxf )(

)()(

)()(

Monte Carlo Principle 1• The idea of Monte Carlo simulation is to draw an independent and identically distributed (i.i.d.) set of samples

{x(1), x(2),…, x(N)} from a target density p(x) defined on a high-dimensional space X. These N samples can be used to approximate the target density with the following empirical point-mass function

The convergence is almost sure (a.s.) and denotes the delta-Dirac mass located at x(i).Think about pN(x) as a histogram or kernel density estimate.

)()(1

)(1

)( xpxN

xp NN

ixN i

)()( xix

)()()(1

)( .).(

1

)()( xpxxw

Nxp saN

N

ix

iN i

Monte Carlo = Sample based pdf approximation

Regions of high density have many samples and high weights of samples.

Discrete approximation of a continuous pdf:

Samples for a Gaussian Matlab demo

Samples for exponential pdf Matlab demo

Delta-Dirac mass

• As a probability measure, the delta measure is characterized by its cumulative distribution function, which is the Heaviside function (or unit step function)

Plot of H0(z) and plot of Hc(0)

)(zc

)()( zHdz

dz cc

and 1)(

dzzc

cz

czzc 0)(

cz

czzHc 0

1)(

0 0

1 1

Delta-Dirac function

• The Dirac delta function as the limit (in the sense of distributions) of the sequence of Gaussians

)(lim)( 00 xx aa

Kernel Density Estimate• Given is set of iid samples {x(1), x(2),…, x(N)} from a density p(x)

The convergence is almost sure.

)()(1

)(1

)( xpxN

xp NN

ixN i

)()(1

)( 0,)(

1

xpxxKN

xf NiN

iN

Monte Carlo Principle 2• We can approximate the integrals, e.g., expectations (or very large sums) E( f ) with tractable sums EN( f ):

The convergence is almost sure (a.s.)

• The N samples can also be used to obtain a maximum of the objective function p(x)

x

NiN

iN

X

NiN

iN

xpxffExfN

fE

dxxpxffExfN

fE

)()()()(1

)(

)()()()(1

)(

)(

1

)(

1

Theorem 1Let x(i) be iid samples from p(x), then

Proof. By the strong law of large numbers, we have

We observe that µ is the mean or expected value of function f(x) with respect to the probability density function, while µ N is the mean of f values at the N sample points.

This proves the theorem.

X

saNiN

iNN dxxpxffExf

NfE )()()()(

1)( .).()(

1

X

saNiN

iN dxxpxffExf

NfE )()()()(

1)( .).()(

1

Example• It is possible to express all probabilities, integrals, and summations as expectations as we illustrate now.• Consider a problem (which is completely deterministic) of integrating a function f(x) from a to b (as in high-school calculus). This can be expressed as an expectation with

respect to a uniformly distributed, continuous random variable U(a,b) between a and b. U has density function pU(x) = 1/(b - a), so if we rewrite the integral we get

• Let x(i) be iid samples from U, then

b

a

b

a

saNiN

i

xfdxab

xfabxfN

ab)(

1)()()( .).()(

1

b

a

b

a

dxab

xfabxf1

)()()(

Theorem 2• Let x(i) be iid samples from p(x), then

Proof. By Theorem 1, we have

We set X=R and f(x)=Hx(t) for some t, and obtain the cdf of p(x):

Consequently,

and

X

saNiN

iNN dxxpxffExf

NfE )()()()(

1)( .).()(

1

)()(1

)( .).(

1

)( xpxN

xp saNN

ixN i

t

R xXdxxpdxxptHdxxpxf )()()()()(

t

saNN

ix

dxxptHN

i )()(1 .).(

1

)(

)()()(1

)(1 .).(

11

)()( xpdxxpt

tN

tHNt

tsaN

N

ix

N

ix ii

Importance Sampling• Sometimes it is hard or impossible to draw samples from p(x).• We have a proposal distribution q(x) such that its support includes the support of p(x), i.e., p(x) > 0 ⇒ q(x) > 0 and it is easy to

draw samples form q(x).• We draw an iid set of samples {x(1), x(2),…, x(N)} from q(x) and define importance weights as

Then samples

{(x(1), w(x(1))), {(x(2), w(x(2))),…, {(x(N), w(x(N))), }

are weighted samples from p(x).

)(

)()(

)(

)()(

i

ii

xq

xpxw

Theorem 3: Importance Sampling• We have a proposal distribution q(x) such that its support includes the support of p(x), i.e., p(x) >

0 ⇒ q(x) > 0. We draw an iid set of samples {x(1), x(2),…, x(N)} from q(x) and define importance weights as

Then

Proof: By Theorem 1 applied to q(x) and f(x)=h(x)w(x):)()()(

1)( .).(

1

)()( xpxxw

Nxp saN

N

ix

iN i

)(

)()(

xq

xpxw

XX

saNiiN

i

dxxpxhdxxqxq

xpxhxwxh

N)()()(

)(

)()()()(

1 .).()()(

1

We set X=R and and h(x)=Hx(t) and obtain the cdf of p(x):

Finally, we have

t

R xXdxxpdxxptHdxxpxh )()()()()(

)()()()(1

)()(1 .).()(

1

)(

1

)()( xpdxxpt

xwtN

xwtHNt

tsaNi

N

ix

iN

ix ii

Importance Sampling 2• When the normalizing constant of p(x) is unknown, it is still possible to apply the importance sampling method:• We draw an iid set of samples {x(1), x(2),…, x(N)} from q(x)

and define importance weights as

Then samples are weighted samples from p(x).

Before

now

N

i

i

ii

xw

xwxw

1

)(

)()(

)(

)()(~

Ni

ii xwx 1)()( ))}(~,{(

X

saNiiN

i

dxxqxwxhxwxhN

)()()()()(1 .).()()(

1

X

XsaN

iN

i

iiN

iN

i

ii

dxxqxw

dxxqxwxh

xwN

xwxhN

xwxhN )()(

)()()(

)(1

)()(1

)(~)(1 .).(

)(

1

)()(

1

1

)()(

Sampling Importance Resampling (SIR)• We draw an iid set of samples {x(1), x(2),…, x(N)} from q(x) and compute the importance weights as

• Normalize the weights as

• Resample from the distribution with probabilities given by the weights and again normalize the weights.

We obtain weighted samples from p(x):

N

i

i

ii

xw

xwxw

1

)(

)()(

)(

)()(~

Ni

ii xwx 1)()( ))}(~,{(

)(

)()(

)(

)()(

i

ii

xq

xpxw

Ninewnew

ii

xwx 1))}(~,{()()(

)()()(1

)( .).(

1

)()( xpxxw

Nxp saN

N

ix

iN i

Sample based pdf representation

Regions of high density have many samples and high weights of samples.

Discrete approximation of a continuous pdf:

Rejection Sampling 1Sometimes it is hard or impossible to draw samples from p(x).We can sample from a distribution p(x), which is known up to a proportionality constant, by sampling from another easy-to-sample proposal distribution q(x) that satisfies p(x) ≤ Mq(x), M < ∞, using the accept/reject procedure.The accepted x(i ) can be shown to be sampled with probability p(x).

Rejection Sampling 2

Rejection sampling suffers from severe limitations. It is not alwayspossible to bound p(x)/q(x) with a reasonable constant M over the whole space X. If M is too large, the acceptance probability will be too small. This makes the method impractical in high-dimensional scenarios.

Theorem 4: Rejection Sampling (taken from pdf)

Demo AssignmentSuppose that we only have uniform generators at hand and we need samples drawn from a Gaussian distribution, i.e., p(x) = G(0, 1). Use the uniform distribution q(x) = U(-5, 5), to draw samples form G(0, 1).

1.Use rejection sampling to get samples from p(x).2.Use importance sampling to get samples from p(x).

In both cases plot the histogram of samples overlaid on the plot of the original Gaussian. What is the percentage of samples you had to reject with the rejection sampler?

Markov Chain Monte Carlo (MCMC)

MCMC is a strategy for generating samples x(i ) while exploring the state space X using a Markov chain mechanism. This mechanism is constructed so that the chain spends more time in the most important regions. In particular, it is constructed so that the samples x(i ) mimic samples drawn from the target distribution p(x). We recall that we use MCMC when we cannot draw samples from p(x) directly, but can evaluate p(x) up to a normalizing constant.

It is intuitive to introduce Markov chains on finite state spaces, where x(i ) can only take s discrete values x(i ) X = {x∈ 1, x2, . . . , xs}. The stochastic process x(i ) is called a Markov chain if

Metropolis-Hastings algorithm

We obtain a set of samples {x(1), x(2),…, x(N)} from p(x),as we show on the next set of slides.

Metropolis-Hastings Example. Bimodal target distribution p(x) 0.3 exp(−0.2x∝ 2) + 0.7 exp(−0.2(x − 10)2)Proposal q(x | x(i )) = G(x(i ), 100)

Simulated annealing for global optimization

This technique involves simulating a non-homogeneous Markov chain whose invariant distribution at iteration i is no longer equal to p(x), but to

Our goal is to find a global maximum

Simulated annealing Example. Bimodal target distribution p(x) 0.3 exp(−0.2x∝ 2) + 0.7 exp(−0.2(x − 10)2)Proposal q(x | x(i )) = G(x(i ), 100)

Gibbs SamplerGibbs sampler can be viewed as a special case of the Metropolis-Hastings algorithm. Suppose we have an n-dimensional vector x=(x1, . . . , xj, . . . , xn). and the expressions for the full conditionalsp(x j | x−j) = p(x j | x1, . . . , xj−1, x j+1, . . . , xn).In this case, it is often advantageous to use the proposal distribution q defined for j = 1, . . . , n as

The corresponding acceptance probability is:

)(

)(

)(

),()|(

)(

)(

)(

)()()()(

ij

i

ij

ij

iji

jij xp

xp

xp

xxpxxp

)(

)()|(

*

***

jjj xp

xpxxp

Thus we always accept the new sample.

Gibbs Sampler

Gibbs Sampling

x1

x2

),,,|(~ )()(3

)(21

)1(1

tK

ttt xxxxx

),,,|(~ )()(3

)1(12

)1(2

tK

ttt xxxxπx

),,|(~ )1(1

)1(1

)1(

tK

tK

tK xxxx

Slide by Ce Liu