Monte Carlo Integration I

Monte Carlo Integration I

Digital Image Synthesisg g yYung-Yu Chuang

with slides by Pat Hanrahan and Torsten Moller

Introduction

)ωp,( ooL )ω,p( oeL

iiiio ωθcos)ωp,()ω,ωp,(2

dLf is• The integral equations generally don’t have

analytic solutions, so we must turn to numerical methods.

• Standard methods like Trapezoidal integration p gor Gaussian quadrature are not effective for high-dimensional and discontinuous integrals.g g

Numerical quadrature

• Suppose we want to calculate , but can’t solve it analytically The approximations

b

adxxfI )(

can t solve it analytically. The approximations through quadrature rules have the form

n

iii xfwI

1)(ˆ

which is essentially the weighted sum of samples of the function at various pointsp p

Midpoint rule

convergenceconvergence

Trapezoid rule


Simpson’s rule

• Similar to trapezoid but using a quadratic polynomial approximationpolynomial approximation


assuming f has a continuous fourth derivative.

Curse of dimensionality and discontinuity

• For an sd f i ffunction f,

• If the 1d rule has a convergence rate of O(n-r), the sd rule would require a much larger number (ns) of samples to work as well as the 1d one. Thus, the convergence rate is only O(n-r/s).

• If f is discontinuous, convergence is O(n-1/s) for , g ( )sd.

Randomized algorithms

• Las Vegas v.s. Monte CarloL V l i h i h b • Las Vegas: always gives the right answer by using randomness.

• Monte Carlo: gives the right answer on the average. Results depend on random numbers used, but statistically likely to be close to the right answer.

Monte Carlo integration

• Monte Carlo integration: uses sampling to estimate the values of integrals It only estimate the values of integrals. It only requires to be able to evaluate the integrand at arbitrary points making it easy to implementarbitrary points, making it easy to implementand applicable to many problems.If l d it t th t • If n samples are used, its converges at the rate of O(n-1/2). That is, to cut the error in half, it is

t l t f ti necessary to evaluate four times as many samples.

• Images by Monte Carlo methods are often noisy. Most current methods try to reduce noise.

Monte Carlo methods

• AdvantagesE t i l t– Easy to implement

– Easy to think about (but be careful of statistical bias)R b t h d ith l i t d d – Robust when used with complex integrands and domains (shapes, lights, …)Efficient for high dimensional integrals– Efficient for high dimensional integrals

• Disadvantages– Noisy– Slow (many samples needed for convergence)

Basic concepts

• X is a random variableA l i f i d i bl i • Applying a function to a random variable gives another random variable, Y=f(X).

• CDF (cumulative distribution function) }Pr{)( xXxP

• PDF (probability density function): nonnegative, sum to 1 xdP )(

}{)(

sum to 1

i l if d i bl ξ ( id d dx

xdPxp )()(

• canonical uniform random variable ξ (provided by standard library and easy to transform to th di t ib ti )other distributions)

Discrete probability distributions• Discrete events Xi with probability pi

n0ip

1

1n

ii

p

ip

• Cumulative PDF (distribution)ip

j

P p 1

• Construction of samples:1

j ii

P p

U

• Construction of samples:To randomly select an event,

Select Xi if 1i iP U P 0iP 0

Uniform random variable 3X

Continuous probability distributions

• PDF ( )p x Uniform

( ) 0p x

• CDF1x

(1) 1P ( )P x

0

( ) ( )P x p x dx ( ) Pr( )P x X x

P ( ) ( )X d

0Pr( ) ( )X p x dx

10( ) ( )P P 10

Expected values

• Average value of a function f(x) over some distribution of values p(x) over its domain Ddistribution of values p(x) over its domain D

dxxpxfxfE )()()(

• Example: cos function over [0, π], p is uniform

Dp dxxpxfxfE )()()(

p [ , ], p

01cos)cos( dxxxE 00cos)cos( dxxxp

Variance

• Expected deviation from the expected valueF d l f if i h • Fundamental concept of quantifying the error in Monte Carlo methods

2)()()]([ xfExfExfV

Properties

)()( xfaExafE

ii XfEXfE )()(

ii

)()( 2 xfVaxafV )()( xfVaxafV

22 )()()( xfExfExfV )()()( xfExfExfV

Monte Carlo estimator

• Assume that we want to evaluate the integral of evaluate the integral of f(x) over [a,b]Gi if d • Given a uniform random variable Xi over [a,b], M t C l ti t Monte Carlo estimator

N

XfabF )(

says that the expected

i

iN XfN

F1

)(

says that the expected value E[FN] of the estimator FN equals the estimator FN equals the integral

General Monte Carlo estimator

• Given a random variable X drawn from an arbitrary PDF p(x) then the estimator isarbitrary PDF p(x), then the estimator is

N

iXfF )(1

i i

N XpNF

1 )(

• Although the converge rate of MC estimator is O(N1/2), slower than other integral methods, its ( )converge rate is independent of the dimension, making it the only practical method for high dimensional integral

Convergence of Monte Carlo

• Chebyshev’s inequality: let X be a random variable with expected value μ and variance σ2 variable with expected value μ and variance σ2. For any real number k>0,

2

1}|Pr{|k

kX

• For example, for k= , it shows that at least half of the value lie in the interval

2)2,2(

• Let , the MC estimate FN becomes)(/)( iii XpXfY N1

),(

i

iN YN

F1

1

Convergence of Monte Carlo• According to Chebyshev’s inequality,

1

2][|][|Pr NNN

FVFEF

YVN

YVN

YVN

YN

VFVN

i

N

i

N

iN1111][ 22

Plugging into Chebyshev’s inequality

NNNN i

ii

ii

iN ][1

21

21

• Plugging into Chebyshev’s inequality,

21

][1||Pr YVIFN

So, for a fixed threshold, the error decreases at

||PrN

IFN

, ,the rate N-1/2.

Properties of estimators

• An estimator FN is called unbiased if for all N

That is, the expected value is independent of N.QFE N ][

• Otherwise, the bias of the estimator is defined as

• If the bias goes to zero as N increases the

QFEF NN ][][

• If the bias goes to zero as N increases, the estimator is called consistent

0][li F 0][lim NN

F

QFE ][lim QFE NN

][lim

Example of a biased consistent estimator

• Suppose we are doing antialiasing on a 1d pixel, to determine the pixel value we need to to determine the pixel value, we need to evaluate , where is the filter function with

1

0)()( dxxfxwI )(xw

1

1)( dfilter function with• A common way to evaluate this is

0

1)( dxxw

N

N

i

N

i iiN

Xw

XfXwF 1

)(

)()(

• When N=1, we have i iXw

1)(

XfX )()( IdxxfXfEXw

XfXwEFE

1

011

111 )()]([

)()()(][

Example of a biased consistent estimator

• When N=2, we have

Idxdxxwxw

xfxwxfxwFE

1

0

1

0 2121

22112 )()(

)()()()(][

• However, when N is very large, the bias approaches to zeropp

N

i ii

N

XfXwNF

1

1

)()(1

N

i i

N

XwN 1

)(1

XfXN

1)()(1liIdxxfxw

dxxw

dxxfxw

XwN

XfXwNFE

N

i i

i iiNNN

1

01

0

1

0

1

1)()(

)(

)()(

)(1lim

)()(lim][lim

N i iN 01

)(

Choosing samples

• N

iN X

XfN

F)()(1

• Carefully choosing the PDF from which samples are drawn is an important technique to reduce

i iXpN 1 )(

are drawn is an important technique to reduce variance. We want the f/p to have a low variance Hence it is necessary to be able to variance. Hence, it is necessary to be able to draw samples from the chosen PDF.

• How to sample an arbitrary distribution from a • How to sample an arbitrary distribution from a variable of uniform distribution?– Inversionve s o– Rejection– Transform

Inversion method

• Cumulative probability distribution function

( ) Pr( )P x X x

• Construction of samplesSolve for X=P-1(U)

1

Solve for X P (U)

• Must know:

U

• Must know:1. The integral of p(x)

12. The inverse function P-1(x)X

0

Proof for the inversion method

• Let U be an uniform random variable and its CDF is P (x) x We will show that Y P-1(U) has CDF is Pu(x)=x. We will show that Y=P-1(U) has the CDF P(x).

Proof for the inversion method

• Let U be an uniform random variable and its CDF is P (x) x We will show that Y P-1(U) has CDF is Pu(x)=x. We will show that Y=P-1(U) has the CDF P(x).

)())(()(Pr)(PrPr 1 xPxPPxPUxUPxY u

because P is monotonic,)()( 2121 xPxPxx

Thus, Y’s CDF is exactly P(x).)()( 2121

Inversion method

• Compute CDF P(x)

• Compute P-1(x)

• Obtain ξξ

• Compute Xi=P-1(ξ)

Example: power functionIt is used in sampling Blinn’s microfacet model.

nnxxp )(

Example: power function

A

It is used in sampling Blinn’s microfacet model.

• Assume( ) ( 1) np x n x

11 1

0 0

11 1

nn xx dx

n n

0 0

1( ) nP x x

1 1~ ( ) ( ) nX p x X P U U

Trick (It only works for sampling power distribution)

1 2 1max( , , , , )n nY U U U U

11

n

1

1

Pr( ) Pr( ) n

i

Y x U x x

Example: exponential distribution

useful for rendering participating media.axcexp )(


• Compute P-1(x)• Compute P (x)

Obt i ξ• Obtain ξ• Compute Xi=P-1(ξ)

Example: exponential distribution

useful for rendering participating media.axcexp )(

10

dxce ax ac


0axx as edsaexP 1)(

• Compute P-1(x)

edsaexP 1)(0

1• Compute P (x)

Obt i ξ

)1ln(1)(1 xa

xP

• Obtain ξ• Compute Xi=P-1(ξ) ln1)1ln(1X )(

aa

Rejection method

• Sometimes, we can’t integrate into CDF or invert CDF

1

( )I f x dx

invert CDF

0

( )f

dx dy

( )y f x

• Algorithm( )y f x

dx dy

• Algorithm

Pick U1 and U2Accept U if U < f(U )Accept U1 if U2 < f(U1)

• Wasteful? Efficiency = Area / Area of rectangle• Wasteful? Efficiency = Area / Area of rectangle

Rejection method

• Rejection method is a dart-throwing method without performing integration and inversion without performing integration and inversion.

1. Find q(x) so that p(x)<Mq(x)2. Dart throwing

a. Choose a pair (X, ξ), where X is sampled from q(x)b. If (ξ<p(X)/Mq(X)) return X

• Equivalently, we pick a qu vale tly, we p c a point (X, ξMq(X)). If it lies beneath (X)it lies beneath p(X)then we are fine.

Why it works

• For each iteration, we generate Xi from q. The sample is returned if ξ<p(X)/Mq(X) which sample is returned if ξ<p(X)/Mq(X), which happens with probability p(X)/Mq(X). S th b bilit t t i • So, the probability to return x is

xpxpxq )()()(

whose integral is 1/M

MxMqxq

)()(

whose integral is 1/M• Thus, when a sample is returned (with

probability 1/M) X is distributed according to probability 1/M), Xi is distributed according to p(x).

Example: sampling a unit diskvoid RejectionSampleDisk(float *x, float *y) {

float sx, sy;, y;do {

sx = 1.f -2.f * RandomFloat();sy = 1.f -2.f * RandomFloat();

} while (sx*sx + sy*sy > 1.f)*x = sx; *y = sy;*x = sx; *y = sy;

}

π/4～78.5% good samples, gets worse in higher dimensions, for example, for sphere, π/6～52.4% dimensions, for example, for sphere, π/6 52.4%

Transformation of variables

• Given a random variable X from distribution px(x)to a random variable Y y(X) where y is one toto a random variable Y=y(X), where y is one-to-one, i.e. monotonic. We want to derive the distribution of Y p (y)distribution of Y, py(y).

• )(}Pr{)}(Pr{))(( xPxXxyYxyP xy P (x)

• PDF:

dxxdP

dxxydP xy )())((

x

Px(x)

dxdx

)(xpdyydPdyyp y )()(

xPy(y)

)(xpxdxy

dydxyyp y

y )(

)()(1

xpdyyp

y

)()( xpdxyyp xy

Example

xxpx 2)( XY sin

2

11

1sin2

cos2)()(cos)(

yy

xxxpxyp xy

1cos yx

Transformation method

• A problem to apply the above method is that we usually have some PDF to sample from not we usually have some PDF to sample from, not a given transformation.Gi d i bl X ith ( ) d • Given a source random variable X with px(x) and a target distribution py(y), try transform X into t th d i bl Y th t Y h th to another random variable Y so that Y has the distribution py(y).

• We first have to find a transformation y(x) so that Px(x)=Py(y(x)). Thus, y

))(()( 1 xPPxy xy

Transformation method

• Let’s prove that the above transform works.W fi h h d i bl ( )We first prove that the random variable Z= Px(x) has a uniform distribution. If so, then )(1 ZPy

should have distribution Py from the inversion method.

Thus Z is uniform and the transformation works xxPPxPXxXPxZ xxxx ))(()(Pr)(PrPr 11

Thus, Z is uniform and the transformation works.• It is an obvious generalization of the inversion

method in which X is uniform and P (x)=xmethod, in which X is uniform and Px(x)=x.

Example

xxpx )( yy eyp )(y

Example

xxpx )( yy eyp )(y

2)(

2xxPx yy eyP )(

2yyPy ln)(1

2lnln2)2

ln())(()(2

1 xxxPPxy xy

Thus, if X has the distribution , then the

2

xxpx )(random variable has the distribution2lnln2 XY

x

yy eyp )(

Multiple dimensions

We often need the other way around,

Spherical coordinates

• The spherical coordinate representation of directions is idirections is

sinsincossin

ryrx

cosrz

sin|| 2rJ sin|| rJT

),,(sin),,( 2 zyxprrp

Spherical coordinates

• Now, look at relation between spherical directions and a solid anglesdirections and a solid angles

ddd sin• Hence, the density in terms of ,

dpddp )(),( dpddp )(),(

)(sin),( pp )(),( pp

Multidimensional sampling

• Separable case: independently sample X from pxand Y from p p(x y) p (x)p (y)and Y from py. p(x,y)=px(x)py(y)

• Often, this is not possible. Compute the i l d it f ti ( ) fi tmarginal density function p(x) first.

dyyxpxp )()(

• Then compute the conditional density function

dyyxpxp ),()(

• Then, compute the conditional density function

)(),()|( yxpxyp

• Use 1D sampling with p(x) and p(y|x).)(

)|(xp

yp

Sampling a hemisphere

• Sample a hemisphere uniformly, i.e. cp )(


• Sample a hemisphere uniformly, i.e. cp )(

21)( p )(1 p

21

c

• Sample first

2sin),( p

sin2

sin),()(22

ddpp

• Now sampling

200

21

)(),()|(

ppp

2)(p


• Now, we use inversion technique in order to sample the PDF’ssample the PDF s

cos1''sin)( dP cos1sin)(0

dP

'1)|( dP

• Inverting these:

2'

2)|(

0

dP

• Inverting these:

11cos

2

1

2


• Convert these to Cartesian coordinate

11cos

212 1)2cos(cossin x

2

1

2cos

212 1)2sin(sinsin

y

Si il d i ti f f ll h

1cos z

• Similar derivation for a full sphere

Sampling a disk

RIGHT Equi-ArealWRONG Equi-Areal RIGHT Equi ArealWRONG Equi Areal

1

2

2 Ur U

12 U

r U

2U2r U

Sampling a disk

RIGHT Equi-ArealWRONG Equi-Areal RIGHT Equi ArealWRONG Equi Areal

12 U 12 U

2r U2r U

Sampling a disk

• Uniform1),( yxp

ryxrprp ),(),(

• Sample r first.

d 2)()(2

• Then sample

rdrprp 2),()(0

• Then, sample .

21

)(),()|(

rprprp

• Invert the CDF.

2)(rp

2)( rrP )|( rP)( rrP

2

)|( rP

r 2 1r 22

Shirley’s mapping

1r U

2

14UU

Sampling a triangle

0u ( ,1 )u u

01

vu v

v

1u v

u1

121 1 1 (1 ) 1u u

1u v

0 0 00

(1 ) 1(1 )2 2uA dv du u du

( ) 2p u v( , ) 2p u v

Sampling a triangle

• Here u and v are not independent! ( , ) 2p u v

),()|( vupvup

• Conditional probability

d)()( )()|(

vpvup

1

( ) 2 2(1 )u

d

dvvupup ),()(

0 2( ) 2(1 ) (1 )u

P u u du u 0

( ) 2 2(1 )p u dv u 0 11u U

1( | )p v u

0 00( ) 2(1 ) (1 )P u u du u

0 1 2v U U

00 0 00 0

1( | ) ( | )(1 ) (1 )

o ov v vP v u p v u dv dvu u

( | )(1 )

p v uu

0 00 0(1 ) (1 )u u

Cosine weighted hemisphere

cos)( p

dp )(1

2

0 0

2 sincos1 ddc 0 0

2 sincos21

dc 01c

ddd sin

sincos1),( p


sincos1),( p

sincos),(p

2ii2i1)(2

d

2sinsincos2sincos)(0

dp

1)(p

21

)(),()|(

ppp

1212cos

21)( P )21(cos

21

11

22

2)|( P

2

22 22)|(

22


• Malley’s method: uniformly generates points on the unit disk and then generates directions by the unit disk and then generates directions by projecting them up to the hemisphere above it.

Vector CosineSampleHemisphere(float u1 float u2){Vector CosineSampleHemisphere(float u1,float u2){Vector ret;ConcentricSampleDisk(u1 u2 &ret x &ret y);ConcentricSampleDisk(u1, u2, &ret.x, &ret.y);ret.z = sqrtf(max(0.f,1.f - ret.x*ret.x -

ret.y*ret.y));ret.y ret.y));return ret;

}}

r


• Why does Malley’s method work?U i di k li r)(• Unit disk sampling

• Map to hemisphere

rp ),(

),(sin),( r

i),( rY ),( XT

sinr

)()())(( 1 xpxJxTp xTy

)()())(( pp xTy

cos0cos

)(

xJ cos10

)(

xJT


),( rY ),( XT

sinr),( ),(

)()())(( 1 xpxJxTp )()())(( xpxJxTp xTy

0cos

cos100cos

)(

xJT

sincos),(),( rpJp T ),(),( pp T

Sampling Phong lobe

np cos)(

ncp cos)(

2 2/

1sincos ddc n

0 0

0

1coscos2 dc n 12

c

1cos

1coscos2

dc 11

n1n

21

nc

sincos1)( nnp

sincos

2),(p

Sampling Phong lobe

sincos2

1),( nnp

sincos)1(sincos2

1)(2

0

nn ndnp

sincos)1()'('

0

n dnP

cos)1(coscos)1('cos1'

0

n

n ndn

'cos1

1)1(coscos)1(

11cos0

n

nndn

cos1

11

1cos n

Sampling Phong lobe

sincos2

1),( nnp

1sincos),()|( 21pp

nn

2

'1

2sincos)1()()|(

'

2

np

p n

221)|'(

0

dP

22

Sampling Phong lobe

cosine-weighted hemisphereWhen n=1, it is actually equivalent to

)2,(cos),( 1,n 211

211 2),21(cos

21),(

212cos

21)( P 21 cos1cos1)( nP

222 cos1sin21)sin21(

21

212cos

21

Piecewise-constant 2d distributions

• Sample from discrete 2D distributions. Useful for texture maps and environment lightsfor texture maps and environment lights.

• Consider f(u, v) defined by a set of nunv values f[ ]f[ui, vj].

• Given a continuous [u, v], we will use [u’, v’] to denote the corresponding discrete (ui, vj) indices.


1 1

],[1),(u vn n

jif vufdudvvufIintegral 0 0

],[),(i j

jivu

f fnn

f

vufvuf '')(

integral

i j jivu vufnn

vufdudvvuf

vufvup,)(1

,),(

),(),(pdf

i iu vufn

d

',)1()()(marginal

f

i

Iduvupvp

),()(marginaldensity

]'[]','[

)(),()|(

vpIvuf

vpvupvup fconditional

probability ][)( pp


),( vuf Distribution2D

low-resmarginal

low-resconditionalmarginal

density)(vp

conditionalprobability

)|( vup)(vp )|( vup

Metropolis sampling

• Metropolis sampling can efficiently generate a set of samples from any non negative function fset of samples from any non-negative function frequiring only the ability to evaluate f.Di d t i l i th • Disadvantage: successive samples in the sequence are often correlated. It is not possible t th t ll b f l to ensure that a small number of samples generated by Metropolis is well distributed over th d i Th i t h i lik the domain. There is no technique like stratified sampling for Metropolis.

Metropolis sampling

• Problem: given an arbitrary function

assuming

generate a set of samples

Metropolis sampling

• StepsG i i i l l – Generate initial sample x0

– mutating current sample xi to propose x’– If it is accepted, xi+1 = x’

Otherwise xi 1 = xiOtherwise, xi+1 xi

• Acceptance probability guarantees distribution is the stationary distribution fis the stationary distribution f

Metropolis sampling

• Mutations propose x’ given xi

• T(x→x’) is the tentative transition probability density of proposing x’ from xy p p g

• Being able to calculate tentative transition probability is the only restriction for the choice probability is the only restriction for the choice of mutations

• a(x→x’) is the acceptance probability of accepting the transition

• By defining a(x→x’) carefully, we ensure

Metropolis sampling

• Detailed balance

stationary distributionstationary distribution

Pseudo code

Pseudo code (expected value)

Binary example I

Binary example II

Acceptance probability

• Does not affect unbiasedness; just varianceW i i h b i i • Want transitions to happen because transitions are often heading where f is large

• Maximize the acceptance probability– Explore state space better– Reduce correlation

Mutation strategy

• Very free and flexible; the only requirement is to be able to calculate transition probabilityto be able to calculate transition probability

• Based on applications and experience• The more mutation, the better• Relative frequency of them is not so importantq y p

Start-up bias

• Using an initial sample not from f’s distribution leads to a problem called start up bias leads to a problem called start-up bias.

• Solution #1: run MS for a while and use the t l th i iti l l t t t current sample as the initial sample to re-start

the process.– Expensive start-up cost– Need to guess when to re-start

• Solution #2: use another available sampling method to start

1D example

1D example (mutation)

1D example

mutation 1 mutation 210,000 iterations

1D example

mutation 1 mutation 2300,000 iterations

1D example

mutation 1 90% mutation 2+ 10% mutation 1

Periodically using uniform mutations increases ergodicity

2D example (image copy)



1 sample i l

8 samples i l

256 samples i lper pixel per pixel per pixel

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Monte Carlo Integration I