### TL;DR
The idea of Monte Carlo (MC) methods is to study a sys...
Nicholas Metropolis is a Greek-America physicist. He is know for hi...
The **Alamogordo test refers to the first detonation of a nuclear b...
### ENIAC
***(Electronic Numerical Integrator And Computer)***
...
The ***lucky numbers sieve*** was invented in the 1950s by a group ...
The term Monte Carlo was coined by N. Metropolis and refers to the ...
A pseudorandom number is a number that appears to be random but is ...
### FERMIAC
FERMIAC was an analog computer invented by physicist...
#### MANIAC
***(Mathematical and Numerical Integrator and Calcula...
THE BEGINNING
of the
MONTE CARLO METHOD
by N. Metropolis
T
he year was 1945. Two earth-
shaking events took place: the
successful test at Alamogordo
and the building of the first elec-
tronic computer. Their combined impact
was to modify qualitatively the nature of
global interactions between Russia and
the West. No less perturbative were the
changes wrought in all of academic re-
search and in applied science. On a less
grand scale these events brought about a
renascence of a mathematical technique
known to the old guard as statistical sam-
pling; in its new surroundings and owing
to its nature, there was no denying its new
name of the Monte Carlo method.
This essay attempts to describe the de-
tails that led to this renascence and the
roles played by the various actors. It is
appropriate that it appears in an issue ded-
icated to Stan Ulam.
Los Alamos Science
Special Issue 1987
Some Background
Most of us have grown so blase about
computer developments and capabilities
-even some that are spectacular—that
it is difficult to believe or imagine there
was a time when we suffered the noisy,
painstakingly slow, electromechanical de-
vices that chomped away on punched
cards. Their saving grace was that they
continued working around the clock, ex-
cept for maintenance and occasional re-
pair (such as removing a dust particle
from a relay gap). But these machines
helped enormously with the routine, rela-
tively simple calculations that led to Hi-
roshima.
The ENIAC. During this wartime pe-
riod, a team of scientists, engineers, and
technicians was working furiously on the
first electronic computer—the ENIAC—
at the University of Pennsylvania in Phil-
adelphia. Their mentors were Physicist
First Class John Mauchly and Brilliant
Engineer Presper Eckert. Mauchly, fa-
miliar with Geiger counters in physics
laboratories, had realized that if electronic
circuits could count, then they could do
arithmetic and hence solve, inter
alia, dif-
ference equations—at almost incredible
speeds! When he’d seen a seemingly
limitless array of women cranking out
firing tables with desk calculators, he’d
been inspired to propose to the Ballistics
Research Laboratory at Aberdeen that an
electronic computer be built to deal with
these calculations.
John von Neumann, Professor of Math-
ematics at the Institute for Advanced
Study, was a consultant to Aberdeen and
to Los Alamos. For a whole host of
125
Monte Carlo
reasons, he had become seriously inter-
ested in the thermonuclear problem being
spawned at that time in Los Alamos by
a friendly fellow-Hungarian scientist, Ed-
ward Teller, and his group. Johnny (as he
was affectionately called) let it be known
that construction of the ENIAC was near-
ing completion, and he wondered whether
Stan Frankel and I would be interested
in preparing a preliminary computational
model of a thermonuclear reaction for the
ENIAC. He felt he could convince the
authorities at Aberdeen that our problem
could provide a more exhaustive test of
the computer than mere firing-table com-
putations. (The designers of the ENIAC
had wisely provided for the capability of
much more ambitious versions of firing
tables than were being arduously com-
puted by hand, not to mention other quite
different applications.) Our response to
von Neumann’s suggestion was enthusi-
astic, and his heuristic arguments were
accepted by the authorities at Aberdeen.
In March, 1945, Johnny, Frankel, and I
visited the Moore School of Electrical En-
gineering at the University of Pennsylva-
nia for an advance glimpse of the ENIAC.
We were impressed. Its physical size
was overwhelming—some 18,000 double
triode vacuum tubes in a system with
500,000 solder joints. No one ever had
such a wonderful toy!
The staff was dedicated and enthusi-
astic; the friendly cooperation is still re-
membered. The prevailing spirit was akin
to that in Los Alamos. What a pity that a
war seems necessary to launch such revo-
lutionary scientific endeavors. The com-
ponents used in the ENIAC were joint-
army-navy (JAN) rejects. This fact not
only emphasizes the genius of Eckert and
Mauchly and their staff, but also suggests
that the ENIAC was technically realizable
even before we entered the war in Decem-
ber, 1941.
After becoming saturated with indoc-
trination about the general and detailed
structure of the ENIAC, Frankel and I re-
turned to Los Alamos to work on a model
126
that was realistically calculable. (There
was a small interlude at Alamogordo!)
The war ended before we completed our
set of problems, but it was agreed that we
continue working.
Anthony Turkevich
joined the team and contributed substan-
tially to all aspects of the work. More-
over, the uncertainty of the first phase of
the postwar Los Alamos period prompted
Edward Teller to urge us not only to com-
plete the thermonuclear computations but
to document and provide a critical review
of the results.
The Spark. The review of the ENIAC
results was held in the spring of 1946
at Los Alamos. In addition to Edward
Teller, the principals included Enrico Fer-
mi, John von Neumann, and the Direc-
tor, Norris Bradbury. Stanley Frankel,
Anthony Turkevich, and I described the
ENIAC, the calculations, and the con-
clusions. Although the model was rel-
atively simple, the simplifications were
taken into account and the extrapolated
results were cause for guarded optimism
about the feasibility of a thermonuclear
weapon.
Among the attendees was Stan Ulam,
who had rejoined the Laboratory after
a brief time on the mathematics faculty
at the University of Southern California.
Ulam’s personality would stand out in
any community, even where “characters”
abounded. His was an informal nature; he
would drop in casually, without the usual
amenities. He preferred to chat, more or
less at leisure, rather than to dissertate.
Topics would range over mathematics,
physics, world events, local news, games
of chance, quotes from the classics—all
treated somewhat episodically but always
with a meaningful point. His was a mind
ready to provide a critical link.
During his wartime stint at the Labora-
tory, Stan had become aware of the elec-
tromechanical computers used for implo-
sion studies, so he was duly impressed,
along with many other scientists, by the
speed and versatility of the ENIAC. In ad-
Stanislaw Ulam
dition, however, Stan’s extensive mathe-
matical background made him aware that
statistical sampling techniques had fallen
into desuetude because of the length and
tediousness of the calculations. But with
this miraculous development of the
ENIAC—along with the applications Stan
must have been pondering—it occurred to
him that statistical techniques should be
resuscitated, and he discussed this idea
with von Neumann. Thus was triggered
the spark that led to the Monte Carlo
method.
The Method
The spirit of this method was consis-
tent with Stan’s interest in random pro-
cesses—from the simple to the sublime.
He relaxed playing solitaire; he was stim-
ulated by playing poker; he would cite
the times he drove into a filled parking
lot at the same moment someone was ac-
commodatingly leaving. More seriously,
he created the concept of “lucky num-
bers,” whose distribution was much like
that of prime numbers; he was intrigued
by the theory of branching processes and
Monte Carlo
contributed much to its development, in-
cluding its application during the war to
neutron multiplication in fission devices.
For a long time his collection of research
interests included pattern development in
two-dimensional games played according
to very simple rules. Such work has lately
emerged as a cottage industry known as
cellular automata.
John von Neumann saw the relevance
of Ulam’s suggestion and, on March 11,
1947, sent a handwritten letter to Robert
Richtmyer, the Theoretical Division lead-
er (see “Stan Ulam, John von Neumann,
and the Monte Carlo Method”). His let-
ter included a detailed outline of a pos-
sible statistical approach to solving the
problem of neutron diffusion in fission-
able material.
Johnny’s interest in the method was
contagious and inspiring. His seemingly
relaxed attitude belied an intense interest
and a well-disguised impatient drive. His
talents were so obvious and his coopera-
tive spirit so stimulating that he garnered
the interest of many of us. It was at that
time that I suggested an obvious name
for the statistical method—a suggestion
not unrelated to the fact that Stan had an
uncle who would borrow money from rel-
atives because he “just had to go to Monte
Carlo.” The name seems to have endured.
The spirit of Monte Carlo is best con-
veyed by the example discussed in von
Neumann’s letter to Richtmyer. Consider
a spherical core of fissionable material
surrounded by a shell of tamper material.
Assume some initial distribution of neu-
trons in space and in velocity but ignore
radiative and hydrodynamic effects. The
idea is to now follow the development
of a large number of individual neutron
chains as a consequence of scattering, ab-
sorption, fission, and escape.
At each stage a sequence of decisions
has to be made based on statistical prob-
abilities appropriate to the physical and
geometric factors. The first two decisions
occur at time
t = O, when a neutron is se-
lected to have a certain velocity and a cer-
tain spatial position. The next decisions
are the position of the first collision and
the nature of that collision. If it is deter-
mined that a fission occurs, the number of
emerging neutrons must be decided upon,
and each of these neutrons is eventually
followed in the same fashion as the first.
If the collision is decreed to be a scatter-
ing, appropriate statistics are invoked to
determine the new momentum of the neu-
John von Neumann
tron. When the neutron crosses a material
boundary, the parameters and characteris-
tics of the new medium are taken into ac-
count. Thus, a genealogical history of an
individual neutron is developed. The pro-
cess is repeated for other neutrons until a
statistically valid picture is generated.
Random Numbers. How are the vari-
ous decisions made? To start with, the
computer must have a source of uni-
formly distributed psuedo-random num-
bers. A much used algorithm for gener-
ating such numbers is the so-called von
Neumann “middle-square digits.” Here,
an arbitrary n-digit integer is squared,
creating a 2n-digit product. A new in-
teger is formed by extracting the middle
n-digits from the product. This process
is iterated over and over, forming a chain
127
Monte Carlo
I
I
1
example, see the section entitled “The
Monte Carlo Method” in “A Primer on
Probability, Measure, and the Laws of
Large Numbers.”)
Since its inception,
many international conferences have been
held on the various applications of the
method.
Recently, these range from
the conference,
“Monte Carlo Methods
and Applications in Neutronics, Photon-
ics, and Statistical Physics,” at Cadarache
Castle, France, in the spring of 1985 to
the latest at Los Alamos, “Frontiers of
Quantum Monte Carlo,” in September,
1985.
Putting the Method into Practice
Let me return to the historical account.
In late 1947 the ENIAC was to be moved
to its permanent home at the Ballistics
Research Laboratory in Maryland. What
a gargantuan task! Few observers were
of the opinion that it would ever do an-
other multiplication or even an addition.
It is a tribute to the patience and skill
of Josh Gray and Richard Merwin, two
fearless uninitiated, that the move was a
success. One salutary effect of the inter-
ruption for Monte Carlo was that another
distinguished physicist took this occasion
to resume his interest in statistical studies.
Enrico Fermi helped create modern
physics.
Here, we focus on his inter-
est in neutron diffusion during those ex-
citing times in Rome in the early thir-
ties. According to Emilio Segre, Fermi’s
student and collaborator, “Fermi had in-
vented, but of course not named, the
present Monte Carlo method when he was
studying the moderation of neutrons in
Rome. He did not publish anything on
the subject, but he used the method to
solve many problems with whatever cal-
culating facilities he had, chiefly a small
mechanical adding machine.”*
In a recent conversation with Segre, I
Company from From X-Rays to Quarks by Emilio
Segre.
128
learned that Fermi took great delight in
astonishing his Roman colleagues with
his remarkably accurate, “too-good-to-be-
lieve” predictions of experimental results.
After indulging himself, he revealed that
his “guesses” were really derived from
the statistical sampling techniques that he
used to calculate with whenever insomnia
struck in the wee morning hours! And
so it was that nearly fifteen years earlier,
Fermi had independently developed the
Monte Carlo method.
Enrico Fermi
It was then natural for Fermi, during
the hiatus in the ENIAC operation, to
dream up a simple but ingenious ana-
log device to implement studies in neu-
tron transport. He persuaded his friend
and collaborator Percy King, while on a
hike one Sunday morning in the moun-
tains surrounding Los Alamos, to build
such an instrument—later affectionately
called the FERMIAC (see the accompa-
nying photo).
The FERMIAC developed neutron ge-
nealogies in two dimensions, that is, in a
plane, by generating the site of the “next
collision. ”
Each generation was based
on a choice of parameters that charac-
terized the particular material being tra-
versed. When a material boundary was
crossed, another choice was made appro-
priate to the new material. The device
could accommodate two neutron energies,
referred to as “slow” and “fast.” Once
again, the Master had just the right feel
for what was meaningful and relevant to
do in the pursuit of science.
The First Ambitious Test. Much to
the amazement of many “experts,” the
ENIAC survived the vicissitudes of its
200-mile journey. In the meantime Rich-
ard Clippinger, a staff member at Ab-
erdeen, had suggested that the ENIAC
had sufficient flexibility to permit its con-
trols to be reorganized into a more conve-
nient (albeit static) stored-program mode
of operation. This mode would have a
capacity of 1800 instructions from a vo-
cabulary of about 60 arithmetical and log-
ical operations. The previous method of
programming might be likened to a gi-
ant plugboard, that is to say, to a can
of worms. Although implementing the
new approach is an interesting story, suf-
fice it to say that Johnny’s wife, Klari,
and I designed the new controls in about
two months and completed the implemen-
tation in a fortnight. We then had the
opportunity of using the ENIAC for the
first ambitious test of the Monte Carlo
method—a variety of problems in neu-
tron transport done in collaboration with
Johnny.
Nine problems were computed corre-
sponding to various configurations of ma-
terials, initial distributions of neutrons,
and running times.
These problems, as
yet, did not include hydrodynamic or ra-
diative effects, but complex geometries
and realistic neutron-velocity spectra
were handled easily. The neutron histo-
ries were subjected to a variety of statisti-
cal analyses and comparisons with other
approaches.
Conclusions about the effi-
cacy of the method were quite favorable.
It seemed as though Monte Carlo was
here to stay.
Not long afterward, other Laboratory
Monte Carlo
staff members made their pilgrimages to
ENIAC to run Monte Carlo problems.
These included J. Calkin, C. Evans, and
F. Evans, who studied a thermonuclear
problem using a cylindrical model as well
as the simpler spherical one. B. Suydam
and R. Stark tested the concept of artifi-
cial viscosity on time-dependent shocks;
they also, for the first time, tested and
found satisfactory an approach to hydro-
dynamics using a realistic equation of
state in spherical geometry. Also, the dis-
tinguished (and mysterious) mathemati-
cian C. J. Everett was taking an inter-
est in Monte Carlo that would culminate
in a series of outstanding publications in
collaboration with E. Cashwell. Mean-
while, Richtmyer was very actively run-
ning Monte Carlo problems on the so-
called SSEC during its brief existence at
IBM in New York.
In many ways, as one looks back, it
was among the best of times.
Rapid Growth. Applications discussed
in the literature were many and varied
and spread quickly. By midyear 1949 a
symposium on the Monte Carlo method,
sponsored by the Rand Corporation, the
National Bureau of Standards, and the
Oak Ridge Laboratory, was held in Los
Angeles. Later, a second symposium was
organized by members of the Statistical
Laboratory at the University of Florida in
Gainesville.
In early 1952a new computer, the MA-
NIAC, became operational at Los Ala-
mos. Soon after Anthony Turkevich led
a study of the nuclear cascades that result
when an accelerated particle collides with
a nucleus. The incoming particle strikes
a nucleon, experiencing either an elastic
or an inelastic scattering, with the latter
event producing a pion. In this study par-
ticles and their subsequent collisions were
followed until all particles either escaped
from the nucleus or their energy dropped
below some threshold value. The “exper-
iment” was repeated until sufficient statis-
tics were accumulated. A whole series of
target nuclei and incoming particle ener-
gies was examined.
Another computational problem run on
the MANIAC was a study of equations
THE FERMIAC
The Monte Carlo trolley, or FERMIAC, was
invented by Enrico Fermi and constructed
by Percy King. The drums on the trolley
were set according to the material being tra-
versed and a random choice between fast
and slow neutrons. Another random digit
was used to determine the direction of mo-
tion, and a third was selected to give the dis-
tance to the next collision. The trolley was
then operated by moving it across a two-
dimensional scale drawing of the nuclear
device or reactor assembly being studied.
The trolley drew a path as it rolled, stopping
for changes in drum settings whenever a
material boundary was crossed. This infant
computer was used for about two years to
determine, among other things, the change
in neutron population with time in numerous
types of nuclear systems.
of state based on the two-dimensional
motion of hard spheres. The work was
a collaborative effort with the Tellers,
Edward and Mici, and the Rosenbluths,
Marshall and Arianna (see “Monte Carlo
at Work”). During this study a strategy
was developed that led to greater com-
puting efficiency for equilibrium systems
obeying the Boltzmann distribution func-
tion. According to this strategy, if a sta-
tistical “move” of a particle in the sys-
tem resulted in a decrease in the energy
of the system, the new configuration was
accepted. On the other hand, if there was
an increase in energy, the new configu-
ration was accepted only if it survived a
game of chance biased by a Boltzmann
factor. Otherwise, the old configuration
became a new statistic.
It is interesting to look back over two-
score years and note the emergence, rather
early on, of experimental mathematics,
a natural consequence of the electronic
computer.
The role of the Monte Carlo
method in reinforcing such mathematics
seems self-evident. When display units
were introduced, the temptation to exper-
129
Monte Carlo
iment became almost irresistible, at least
for the fortunate few who enjoyed the lux-
ury of a hands-on policy. When shared-
time operations became realistic, exper-
imental mathematics came of age. At
long last, mathematics achieved a certain
parity-the twofold aspect of experiment
and theory-that all other sciences enjoy.
It is, in fact, the coupling of the sub-
tleties of the human brain with rapid
and reliable calculations, both arithmeti-
cal and logical, by the modern computer
that has stimulated the development of
experimental mathematics. This develop-
ment will enable us to achieve Olympian
heights.
The Future
So far I have summarized the rebirth
of statistical sampling under the rubric
of Monte Carlo. What of the future—
perhaps even a not too distant future?
The miracle of the chip, like most mir-
acles, is almost unbelievable. Yet the fan-
tastic performances achieved to date have
not quieted all users. At the same time we
are reaching upper limits on the comput-
ing power of a single processor.
One bright facet of the miracle is the
lack of macroscopic moving parts, which
makes the chip a very reliable bit of
hardware. Such reliability suggests par-
allel processing.
The thought here is
not a simple extension to two, or even
four or eight, processing systems. Such
extensions are adiabatic transitions that,
to be sure, should be part of the im-
mediate, short-term game plan. Rather,
the thought is massively parallel opera-
tions with thousands of interacting pro-
cessors-even millions!
Already commercially available is one
computer, the Connection Machine, with
65,536 simple processors working in par-
allel. The processors are linked in such
a way that no processor in the array is
more than twelve wires away from an-
other and the processors are pairwise con-
nected by a number of equally efficient
routes, making communication
both flex-
ible and efficient. The computer has been
used on such problems as turbulent fluid
flow, imaging processing (with features
analogous to the human visual system),
document retrieval, and “common-sense”
reasoning in artificial intelligence.
One natural application of massive par-
allelism would be to the more ambitious
Monte Carlo problems already upon us.
To achieve good statistics in Monte Carlo
calculations, a large number of “histories”
need to be followed. Although each his-
tory has its own unique path, the under-
lying calculations for all paths are highly
parallel in nature.
Still, the magnitude of the endeavor
to compute on massively parallel devices
must not be underestimated. Some of the
tools and techniques needed are:
●
●
●
●
A high-level language and new archi-
tecture able to deal with the demands
of such a sophisticated language (to the
relief of the user);
Highly efficient operating systems and
compilers;
Use of modern combinatorial theory,
perhaps even new principles of logic,
in the development of elegant, compre-
hensive architectures;
A fresh look at numerical analysis and
the preparation of new algorithms (we
have been mesmerized by serial com-
putation and purblind to the sophistica-
tion and artistry of parallelism).
Where will all this lead? If one were
to wax enthusiastic, perhaps—just per-
haps—a simplified model of the brain
might be studied. These studies, in turn,
might provide feedback to computer ar-
chitects designing the new parallel struc-
tures.
Such matters fascinated Stan Ulam. He
often mused about the nature of memory
and how it was implemented in the brain.
Most important, though, his own brain
possessed the fertile imagination needed
to make substantive contributions to the
very important pursuit of understanding
intelligence.
■
Further Reading
S. Ulam, R. D. Richtmyer, and J. von Neumann.
1947. Statistical methods in neutron diffusion. Los
Alamos Scientific Laboratory report LAMS–551.
This reference contains the von Neumann letter dis-
cussed in the present article.
N. Metropolis and S. Ulam. 1949. The Monte
Carlo method. Journal of the American Statistical
Association 44:335-341.
S. Ulam. 1950. Random processes and transforma-
tions. Proceedings of the International Congress of
Mathematicians 2:264-275.
Los Alamos Scientific Laboratory. 1966. Fermi in-
vention rediscovered at LASL. The Atom, October,
pp. 7-11.
C. C. Hurd. 1985. A note on early Monte Carlo
computations and scientific meetings. Annals of the
History of Computing 7:141–155.
W. Daniel Hillis. 1987. The connection machine.
Scientific American,
June, pp. 108–1 15.
N. Metropolis received his B.S. (1937) and his
Ph.D. ( 1941) in physics at the University of Chi-
cago. He arrived in Los Alamos, April 1943, as
a member of the original staff of fifty scientists.
After the war he returned to the faculty of the
University of Chicago as Assistant Professor. He
came back to Los Alamos in 1948 to form the
group that designed and built MANIAC I and II. (He
chose the name MANIAC in the hope of stopping
the rash of such acronyms for machine names, but
may have, instead, only further stimulated such use.)
From 1957 to 1965 he was Professor of Physics
at the University of Chicago and was the founding
Director of its Institute for Computer Research. In
1965 he returned to Los Alamos where he was made
a Laboratory Senior Fellow in 1980. Although he
retired recently, he remains active as a Laboratory
Senior Fellow Emeritus.
130
Nicholas Metropolis is a Greek-America physicist. He is know for his contributions to the Monte Carlo method and is one of the first “computer programmers”. He headed a team that carried out the first actual Monte Carlo calculations on the ENIAC computer. The Monte Carlo method originated from a collaboration between Metropolis and Stanislaw Ulam.
Learn more here: [Atomic Heritage Foundation - Nicholas Metropolis](https://www.atomicheritage.org/profile/nicholas-metropolis)
### ENIAC
***(Electronic Numerical Integrator And Computer)***
The ENIAC was the first programmable general-purpose electronic computer build during WWII and finished in 1945. The ENIAC was funded by the US army and the total cost was about 487,000 dollars equivalent to 6,887,000 in 2017 dollarS. It was built in secret by the University of Pennsylvania’s Moore School of Electrical Engineering from 1943 to 1945. It was first put to use at the end of 1945.
The specs of the ENIAC:
*Fugure: Programming the ENIAC*
Learn more here:
- [ENIAC Overview](https://www.thocp.net/hardware/eniac.htm)
- [Wikipedia - ENIAC](https://en.wikipedia.org/wiki/ENIAC)
The **Alamogordo test refers to the first detonation of a nuclear bomb**. The code name of the first test was **Trinity** and was carried out by the US Army in the Jornada del Muerto desert on July 16th 1945, as part of the Manhattan Project. You can learn more here: [Trinity (nuclear test).](https://en.wikipedia.org/wiki/Trinity_nuclear_test)
The **first electronic computer was called Electronic Numerical Integrator and Computer - ENIAC**. It was built from 1943 to 1945 by J. Presper Eckert and John V. Mauchly at the University of Pennsylvania and funded by the US army which needed a way to compute ballistics during World War II. The machine wasn't completed until 1945, but then it was used extensively for calculations during the design of the hydrogen bomb.
Learn more here: [ENIAC](https://en.wikipedia.org/wiki/ENIAC)
#### MANIAC
***(Mathematical and Numerical Integrator and Calculator)***
The MANIAC was one of the earliest computers. It was developed at Los Alamos under the direction of Nicholas Metropolis. It was smaller than the ENIAC and was able to store programs (the ENIAC couldn't).
The MANIAC was used by physicists like E. Fermi and E. Teller allowing them to perform simulations much faster in order to gain a better understanding of the behavior of particles. It was used for other innumerable experiments and discoveries. MANIAC became the first computer to play and beat a human, at a chess-like game.
FUN FACT: Metropolis chose the name MANIAC in the hope of stopping the rash of silly acronyms for machine names. It is one of the ancestors of many modern computers.
*Picture: Paul Stein and Nick Metropolis playing modified chess with the MANIAC I*
Learn more here:
- [Metropolis, Monte Carlo and the MANIAC](http://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-UR-86-2600-05)
- [MANIAC I](https://chessprogramming.wikispaces.com/MANIAC+I)
### TL;DR
The idea of Monte Carlo (MC) methods is to study a system by simulating it with the repeated generation of random events in a computer model. Monte Carlo methods are often used in simulations of physical and mathematical systems. The development of MC methods is linked with the development of the general purpose machines and the need to solve problems related to the development of atomic weapons.
The general premise of Monte Carlo methods is remarkably simple:
1. Randomly sample input to the problem
2. Compute an output for each sample
3. Approximate the solution by aggregating the outputs
MC methods in a nutshell:
- the core idea of the Monte Carlo is to learn about a system by simulating it with random sampling
- the development of these methods is linked to the necessity of solving problems related to atomic weapons
- it's the simplest way or even the only feasible way to solve complex problems
- these methods constitute a powerful and flexible approach
- they are applied to physical sciences (widely used at the LHC), engineering, statistics, finance, machine learning
The term Monte Carlo was coined by N. Metropolis and refers to the Monte Carlo Casino in Monaco. It was a nod to a colleague, Stanislaw Ulam, who had an uncle that was a gambling man and would borrow money from the family by saying that “I just have to go to Monte Carlo”. He chose this name because of the apparent element of ‘chance’ in gambling and in Monte Carlo methods.
The ***lucky numbers sieve*** was invented in the 1950s by a group in the Mathematics Division at the Los Alamos National Laboratory.
How it works:
1. Start with a sequence of positive integers listed in the natural order.
2. Remove every other number. The first left-over number is 3.
3. Mark 3 and remove every third number among the remaining ones. The first left-over number past 3 is 7.
4. Mark 7 and remove every seventh number among the remaining ones.
5. Continue this way.
Learn more here:
- [Cut The Knot - Lucky Numbers](https://www.cut-the-knot.org/Curriculum/Algorithms/LuckyNumbers.shtml)
- [Lucky Number](http://mathworld.wolfram.com/LuckyNumber.html)
- [Wikipedia - Lucky Number](https://en.wikipedia.org/wiki/Lucky_number)
A pseudorandom number is a number that appears to be random but is not (unlike dice rolls or lottery tickets). Pseudorandom sequences typically exhibit statistical randomness while being generated by a deterministic causal process. Pseudorandom number generators are mathematical algorithms that produce sequences of numbers that appear to be random, their outputs are periodic, but the period is so big that it's very hard to notice. Modern algorithms for generating pseudorandom numbers are so good that the numbers look like they were really random.
Learn more here:
- [Khan Academy - Pseudorandom number generators](https://www.khanacademy.org/computing/computer-science/cryptography/crypt/v/random-vs-pseudorandom-number-generators)
- [Wikipedia - Pseudorandom number generator](https://en.wikipedia.org/wiki/Pseudorandom_number_generator)
### FERMIAC
FERMIAC was an analog computer invented by physicist Enrico Fermi to aid in his studies of neutron transport. It resembles a trolley car and in order to use it, several adjustable drums are set using pseudorandom numbers.
These random numbers represent:
1. the material being traversed, a random choice is made between fast and slow neutrons
2. the direction of neutron travel
3. the distance traveled to the next collision
Once these settings are chosen, the device is physically driven across a 2-D scale drawing of the nuclear reactor and the paths of neutrons through the various materials are plotted. Whenever a material boundary is crossed, the appropriate drum is adjusted to represent a new pseudorandom digit.
Learn more here: - [Wikipedia - FERMIAC](https://en.wikipedia.org/wiki/FERMIAC)
*FERMIAC, or Monte Carlo trolley is an analog device invented by Enrico Fermi*