Transcript Parallel Programming in C with the Message Passing Interface

Lecture 10 Outline
Monte Carlo methods
 History of methods
 Sequential random number generators
 Parallel random number generators
 Generating non-uniform random numbers
 Monte Carlo case studies

Monte Carlo Methods


Monte Carlo is another name for statistical
sampling methods of great importance to physics
and computer science
Applications of Monte Carlo Method
 Evaluating integrals of arbitrary functions of 6+
dimensions
 Predicting future values of stocks
 Solving partial differential equations
 Sharpening satellite images
 Modeling cell populations
 Finding approximate solutions to NP-hard
problems
An Interesting History of Statistical Physics
• In 1738, Swiss physicist and mathematician Daniel Bernoulli published
Hydrodynamica which laid the basis for the kinetic theory of gases: great
numbers of molecules moving in all directions, that their impact on a
surface causes the gas pressure that we feel, and that what we experience
as heat is simply the kinetic energy of their motion.
• In 1859, Scottish physicist James Clerk Maxwell formulated the
distribution of molecular velocities, which gave the proportion of
molecules having a certain velocity in a specific range. This was the
first-ever statistical law in physics. Maxwell used a simple thought
experiment: particles must move independent of any chosen coordinates,
hence the only possible distribution of velocities must be normal in each
coordinate.
• In 1864, Ludwig Boltzmann, a young student in Vienna, came across
Maxwell’s paper and was so inspired by it that he spent much of his
long, distinguished, and tortured life developing the subject further.
History of Monte Carlo Method



Credit for inventing the Monte Carlo method is shared by Stanislaw
Ulam, John von Neuman and Nicholas Metropolis.
Ulam, a Polish born mathematician, worked for John von Neumann on
the Manhattan Project. Ulam is known for designing the hydrogen
bomb with Edward Teller in 1951. In a thought experiment he
conceived of the MC method in 1946 while pondering the probabilities
of winning a card game of solitaire.
Ulam, von Neuman, and Metropolis developed algorithms for
computer implementations, as well as exploring means of transforming
non-random problems into random forms that would facilitate their
solution via statistical sampling. This work transformed statistical
sampling from a mathematical curiosity to a formal methodology
applicable to a wide variety of problems. It was Metropolis who named
the new methodology after the casinos of Monte Carlo. Ulam and
Metropolis published a paper called “The Monte Carlo Method” in
Journal of the American Statistical Association, 44 (247), 335-341, in
1949.
Errors in Estimation and
Two Important Questions for Monte Carlo



Errors arise from two sources
 Statistical: using finite number of samples to estimate an inifinte
sum.
 Computational: using finite state machines to produce statistically
independent random numbers.
Questions
 To ensure a level of statistical accuracy, i.e., m significant digits
with probability 1-1/n, how many samples are needed?
 Given n samples, how statistically accurate is the estimate?
“the best methods rarely offers more than 2-3 digit accuracy”-- G.
Fishman, Monte Carlo Methods, Springer
Controlling Error


Two tenets from statistics:
 Weak Law of Large Numbers
 value of a sum of i.i.d random variables converges to me n u (
u is mean).
 So as n grows approx error vanishes
 Central Limit Theorem
 Sum converges to a normal distribution with mean nu and
variance n var sqrt(n)
 So as n grows error can be estimated using normal tables and
theorems about tails of distribution.
Caveats
 Convergence rates differ and can be complex
 Results in additional error estimates or need larger sample size
than normal distribution would indicate
Sample Size and Chebyshev’s Theorem

Theorem: If X is a discrete random variable with
mean  and standard deviation , then for every
positive real number
2
P( X    h )  1/ h
or equivalently
P( X    h )  1  (1/ h2 )
That is, the probability that a random variable will
assume a value more than h standard deviations
away from its mean is never greater than the
square of the reciprocal of h.
An Example:

200 years ago, Comte de Buffon, proposed the following problem. If a needle of length l
is dropped at random on the middle of a horizontal surface ruled with parallel lines a
distance d > l apart, what is the probability that the needle will cross one of the lines?

The positioning of the needle relative to nearby lines can be described with a random
vector (A, theta) is uniformly distributed on the region [0,d) [0,pi). Accordingly, it has
probability density function above. The probability that the needle will cross one of the
lines is given by the integral
Estimation and variance reduction

This integral is easily solved to obtain the probability

Suppose Buffon’s experiment is performed with the needle being dropped n times. Let
M be the random variable for the number of times the needle crosses a line,

Certain techniques can be used to reduce variance, e.g., an experimenter Fox used five
inch needle and made the lines on the surface just two inches apart. Now, the needle
could cross as many as three lines in a single toss. In 590 tosses, Fox obtained 939 line
crossings. This was a precursor of today’s variance reduction techniques.
Variance Reduction

There are several ways to reduce the variance
 Importance Sampling
 Stratified Sampling
 Quasi-random Sampling
 Metropolis Random Mutations
Simulation Often rely on Other
Distributions
Analytical transformations
 Box-Muller Transformation
 Rejection method

Analytical Transformation
-probability density function f(x)
-cumulative distribution F(x)
In theory of probability, a quantile function of a distribution is the inverse
of its cumulative distribution function.
Exponential Distribution:
An exponential distribution arises naturally when modeling the time between independent
events that happen at a constant average rate and are memoryless. One of the few cases
where the quartile function is known analytically.
1.0
F 1 (u )  m ln(1  u )
F ( x)  1  e
x/ m
1 x / m
f ( x)  e
m
Example 1:
Produce four samples from an exponential
distribution with mean 3
 Uniform sample: 0.540, 0.619, 0.452, 0.095
 Take natural log of each value and multiply
by -3
 Exponential sample: 1.850, 1.440, 2.317,
7.072

Example 2:





Simulation advances in time steps of 1 second
Probability of an event happening is from an exponential
distribution with mean 5 seconds
What is probability that event will happen in next second?
F(x=1/5) =1 - exp(-1/5)) = 0.181269247
Use uniform random number to test for occurrence of
event (if u < 0.181 then ‘event’ else ‘no event’)
Normal Distributions:
Box-Muller Transformation
Cannot invert cumulative distribution
function to produce formula yielding
random numbers from normal (gaussian)
distribution
 Box-Muller transformation produces a pair
of standard normal deviates g1 and g2 from
a pair of normal deviates u1 and u2

Box-Muller Transformation
repeat
v1  2u1 - 1
v2  2u2 - 1
r  v12 + v22
until r > 0 and r < 1
f  sqrt (-2 ln r /r )
g1  f v1
g2  f v2
This is a consequence of
the fact that the chisquare distribution with
two degrees of freedom
is an easily-generated
exponential random
variable.
Example

Produce four samples from a normal
distribution with mean 0 and standard
deviation 1
u1
u2
v1
v2
r
f
g1
g2
0.234
0.784
-0.532
0.568
0.605
1.290
-0.686
0.732
0.824
0.039
0.648
-0.921
1.269
0.430
0.176
-0.140
-0.648
0.439
1.935
-0.271
-1.254
Von Neumann’s Rejection Method
Case Studies (Topics Introduced)
Temperature inside a 2-D plate (Random
walk)
 Two-dimensional Ising model
(Metropolis algorithm)
 Room assignment problem (Simulated
annealing)
 Parking garage (Monte Carlo time)
 Traffic circle (Simulating queues)

Temperature Inside a 2-D Plate
Random walk
Example of Random Walk
0  u  1  4u  {0,1,2,3}
132
Parking Garage
Parking garage has S stalls
 Car arrivals fit Poisson distribution with
mean A: Exponentially distributed interarrival times
 Stay in garage fits a normal distribution
with mean M and standard deviation M/S

Implementation Idea
Times Spaces Are Available
101.2
142.1
70.3
91.7
223.1
Current Time
Car Count
Cars Rejected
64.2
15
2
Summary

Concepts revealed in case studies
 Monte Carlo time
 Random walk
 Metropolis algorithm
 Simulated annealing
 Modeling queues