[HN Gopher] The beginning of the Monte Carlo method (1987) [pdf]
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       The beginning of the Monte Carlo method (1987) [pdf]
        
       Author : swibbler
       Score  : 113 points
       Date   : 2022-09-18 11:15 UTC (11 hours ago)
        
 (HTM) web link (lib-www.lanl.gov)
 (TXT) w3m dump (lib-www.lanl.gov)
        
       | radford-neal wrote:
       | It's not a very good history.
       | 
       | It says "In the late 1940s, Stanislaw Ulam invented the modern
       | version of the Markov Chain Monte Carlo method", but as far as I
       | know, this is incorrect. He invented a Monte Carlo method, but
       | not a Markov chain Monte Carlo method. Markov chain Monte Carlo
       | is generally attributed to Metropolis, Rosenbluth, Rosenbluth,
       | Teller, and Teller. See https://en.wikipedia.org/wiki/Metropolis-
       | Hastings_algorithm
       | 
       | The article fails even to distinguish simple Monte Carlo based on
       | independently sampled points from Markov chain Monte Carlo. It
       | seems rather confused in other respects too, such as in its
       | discussion of "mean field" methods.
        
         | [deleted]
        
         | dang wrote:
         | This comment was posted when the URL at the top was
         | https://en.wikipedia.org/wiki/Monte_Carlo_method#History.
         | 
         | We've since changed it to the URL suggested by sampo at
         | https://news.ycombinator.com/item?id=32889436.
        
         | sampo wrote:
         | Here is an article "The beginning of the Monte Carlo method" by
         | N. Metropolis: https://lib-www.lanl.gov/cgi-
         | bin/getfile?00326866.pdf
        
           | dang wrote:
           | Nice. We've changed to that from
           | https://en.wikipedia.org/wiki/Monte_Carlo_method#History
           | above. Thanks!
        
         | ckrapu wrote:
         | What do you think comes next after HMC/NUTS for general purpose
         | turn-key sampling?
        
       | bardcore wrote:
       | I didn't realize how integral Monte Carlo sims were to our early
       | advances in nuclear technology. It makes sense to me though- it
       | seems like the Monte Carlo method lets you punch above your
       | weight class in terms of measuring and predicting phenomena that
       | are too complex, or too expensive to deterministically model.
       | 
       | My intuition tells me that it's effectiveness would fall off as
       | the complexity of the in/out relationship scales. Is this true?
       | Or can sufficient sample density overcome arbitrary levels of
       | that type of complexity?
        
         | vasco wrote:
         | You should be able to model "infinite" complexity, the way
         | people design analog circuits is basically this.
        
           | BOOSTERHIDROGEN wrote:
           | Sorry for stupid question, what is infinite complexity in
           | analog circuit ? Any examples/model ?
        
             | RicoElectrico wrote:
             | I think it's an ad-hoc term. But basically, MC simulations
             | are needed because circuit elements such as transistors,
             | resistors may be mismatched, for example Vth in MOS
             | transistors. This can create input offsets in op-amps, or
             | timing differences in logic. You can run exactly the same
             | simulations as normally (DC operating point, AC transfer
             | function, time-domain with many thousands of points) just
             | over and over with new random parameters according to the
             | distribution estimated in device characterization (I think
             | mostly Gaussian). Transistors models like BSIM are crazy
             | complicated these days and there's no way to find an
             | analytical solution for all that.
        
         | mjb wrote:
         | > the Monte Carlo method lets you punch above your weight class
         | in terms of measuring and predicting phenomena that are too
         | complex, or too expensive to deterministically model.
         | 
         | Yes, this is exactly why I like it. At AWS, we've used Monte
         | Carlo simulations quite extensively to model the behavior of
         | complex distributed systems and distributed databases. These
         | are typically systems with complex interactions between many
         | components, each linked by a network with complex behavior of
         | its own. Latency and response time distributions are typically
         | multi-modal, and hard to deal with analytically.
         | 
         | One direction I'm particularly excited by in this niche is
         | converging simulation tools and model checking tools. For
         | example, we could have a tool like P use the same specification
         | for exhaustive model checking, fuzzing invariants, and doing MC
         | (and MCMC) to produce statistical models of things like latency
         | and availability.
        
           | mjb wrote:
           | A while ago I wrote this as a simple introduction to applying
           | MC methods in distributed systems:
           | https://brooker.co.za/blog/2022/04/11/simulation.html
        
         | NohatCoder wrote:
         | It is hard to generalise about the suitability of Monte Carlo
         | methods. In practical applications it is almost always used in
         | hybrid systems, combined with analytical methods and problem
         | specific short cuts. How one should apply Monte Carlo methods
         | to a problem tends to be an open ended question.
        
       | aaron695 wrote:
        
       | atan2 wrote:
       | I cannot believe a raw link to Wikipedia made it to the top
       | stories.
        
         | melling wrote:
         | Some people on HN farm for karma points. They frequently post
         | Wikipedia articles. It seems somewhat effective.
         | 
         | There are certain topics that attract a bit of quick attention.
        
           | psd1 wrote:
           | A high-karma Reddit account has monetary value, you can sell
           | it to a bot operator.
           | 
           | Does an HN account have any value, or do you think karma
           | farmers are just in it for the ego boost?
        
           | dcminter wrote:
           | I'm sure that's true, but _also_ there 's a lot of very
           | interesting HN-relevant stuff on Wikipedia, and it's
           | perfectly reasonable to share it when you find something
           | pithy.
           | 
           | I've certainly done it and contrariwise have often enjoyed
           | Wikipedia articles (including this one) from other users.
           | 
           | Apropos of which I wish I'd had Wikipedia when I was a kid -
           | I recall being utterly baffled by Brittanica's "explanation"
           | of the term "parsec" and only much later reading a definition
           | that put it in the context of how stellar distances were
           | actually resolved.
           | 
           | Edit: Looking at swibbler's submission history, they're
           | clearly not a karma farmer btw.
        
         | dang wrote:
         | Edit: as if to illustrate the below, someone found a more
         | specific third-party article so we've since switched the URL.
         | More at https://news.ycombinator.com/item?id=32890671.
         | 
         | ------
         | 
         | Happens all the time!
         | https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...
         | 
         | It's a good practice _not_ to link to wikipedia.org when a more
         | in-depth or specific third-party source is available, or if the
         | topic is a well known one (too generic). But that leaves a lot
         | of Wikipedia pages on more obscure topics, and those make fine
         | HN submissions, as long as the topic is of intellectual
         | interest and not particularly correlated with other things. And
         | as long as we don 't overdo it.
         | 
         | Past explanations about this:
         | https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...
         | 
         | https://news.ycombinator.com/item?id=30307077
         | 
         | https://news.ycombinator.com/item?id=23929687
         | 
         | https://news.ycombinator.com/item?id=23249978
        
         | detaro wrote:
         | not unusual at all
        
           | warinukraine wrote:
        
         | speedylight wrote:
         | I've seen it happen quite a bit!
        
         | yesco wrote:
         | Wikipedia posts are my favorite kind of submission
        
         | blooalien wrote:
         | If it's properly fascinating, well written, and informative,
         | why not?
        
         | srvmshr wrote:
         | A while ago, I submitted a raw Wiki link to "Ligne Claire", a
         | drawing style adopted by Herge of "Adventures of Tintin" fame.
         | That made it to #1 and remained for a few hours. It is not very
         | unusual.
        
       | dang wrote:
       | Pretty cool author bio from the end of the article:
       | 
       |  _N. Metropolis received his B.S. (1937) and his Ph.D. (1941) in
       | physics at the University of Chicago. 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._
        
       | alhirzel wrote:
       | It should be noted that modern graphics rendering techniques are
       | a little more accessible and intuitive, while still having the
       | same basic challenges and solutions as the nuclear simulations
       | mentioned in this thread/in the Wikipedia article. Things get
       | even more interesting because quantities are spectral in nature,
       | can be polarized, etc. Wenzel Jakob is doing important work in
       | this area out of EPFL[1].
       | 
       | [1]: https://rgl.epfl.ch/people/wjakob/
        
       | scubakid wrote:
       | It's also super useful for exploring the spectrum of potential
       | outcomes in financial projections / retirement scenarios. People
       | are sometimes tempted to think in the simpler terms of average
       | rates of return and not fully consider issues like sequence risk.
       | MC can help build better intuitions around the real chances of
       | success given a broader range of varying market conditions.
        
       | rahen wrote:
       | For those interested, the first Monte Carlo program, which ran on
       | the ENIAC, has been found and documented (840 instructions long).
       | It was also the first stored program ever run.
       | 
       | https://eniacinaction.com/the-articles/3-los-alamos-bets-on-...
       | 
       | https://eniacinaction.com/wp-content/uploads/2014/02/LosAlam...
        
       | acidburnNSA wrote:
       | Nuclear engineer here (specialty in core design/simulation). It's
       | fun to see the Monte Carlo method be used in so many other fields
       | now. Even in nuclear, deterministic methods are still orders of
       | magnitude faster for most 'normal' reactor analyses on reactor
       | configurations that are common enough to have all the important
       | deterministic effects known. But with computers so fast, it's
       | quite common for people, especially in conceptual design space,
       | to use Monte Carlo methods since it's a lot easier to believe the
       | answer once you get it.
       | 
       | The code MCNP is still the most common nuclear analysis code, and
       | it's directly descended from these original codes from LANL. [1]
       | 
       | There is also a very powerful research code called OpenMC from
       | ANL that anyone can run on their (powerful) computer. [2]
       | 
       | [1] https://mcnp.lanl.gov/reference_collection.html
       | 
       | [2] https://docs.openmc.org/en/stable/
        
         | dav_Oz wrote:
         | >But with computers so fast, it's quite common for people,
         | especially in conceptual design space, to use Monte Carlo
         | methods since it's a lot easier to believe the answer once you
         | get it.
         | 
         | Is it really easier to "believe" an answer on a purely
         | stochastical level? I'm kinda surprised, I would be way more
         | confident (if I were to choose) with answers from deterministic
         | descriptions/equations despite being more abstract and
         | potentially harder to "visualize".
         | 
         | I find more often than not supposed 'comprehensibility' on the
         | surface level to be quite misleading. Of course if one doesn't
         | have clue where to start and enough processing power the Monte
         | Carlo method and alike certainly can help to jumpstart/brute
         | force the process.
        
           | fastneutron wrote:
           | MC tends to be more physically accurate (in the limit of a
           | large number of particle histories) because it can simulate
           | radiation transport in continuous space, energy, and angular
           | distribution. Deterministic methods can be faster, but the
           | discretization process is somewhat of a dark art because the
           | underlying distributions can be highly nonlinear and rapidly
           | varying. A combination of methods with varying levels of
           | fidelity are typically used in real nuclear engineering
           | applications, and are always referenced back to a common
           | suite of experimental benchmarks for validation.
        
           | wnkrshm wrote:
           | You can write down an equation for evaluating a ray-traced
           | picture with perfect mathematical precision, you just cannot
           | evaluate the integral for any scene that is non-trivial. MC
           | is the integration technique for it.
           | 
           | Edit: so you know exactly what you get, if you keep it simple
           | - the gotchas start if you try to be clever and use fewer
           | samples (biased MC)
        
           | acidburnNSA wrote:
           | Yes, definitely. With deterministic methods you have to make
           | all sorts of approximations to discretize the spatial details
           | of the fuel assemblies, the energy space of the neutrons, the
           | angular directions of the neutrons, and so on. The
           | approximations are complex and sensitive. With monte carlo
           | methods you can treat all those things without
           | approximations. Under the hood, both deterministic and monte
           | carlo nucleonics methods are depending on the same
           | measured/interpolated nuclear interaction probability tables
           | (aka nuclear cross sections).
        
         | pixelpoet wrote:
         | That sounds super interesting, how does one get into simulating
         | reactor cores? I'm very familiar with MC methods from computer
         | graphics.
        
           | acidburnNSA wrote:
           | I would definitely start with some of the openmc tutorials.
           | 
           | https://docs.openmc.org/en/stable/usersguide/beginners.html
           | 
           | If you're asking at a higher level, you end up in nuclear
           | engineering school after having a nebulous interest in energy
           | issues.
        
           | eternalban wrote:
           | _An Exact MCNP Modeling of Pebble Bed Reactors_ :
           | 
           | https://www.researchgate.net/publication/264537140_An_Exact_.
           | ..
           | 
           | OP's link was a rabbit hole (in a v. good way), sent me down
           | some paper on the LCG random number generator used for MCNP
           | modelling, which somehow led to that.
           | 
           | (https://mcnp.lanl.gov/pdf_files/la-ur-07-7961.pdf)
        
       | ur-whale wrote:
       | The one thing I love with Monte-Carlo is the way you can use it
       | very simply to give yourself some peace of mind that your
       | probability formula for a finite distribution, derived with sweat
       | and blood using very complicated combinatorics (the kind found in
       | here:
       | https://www.csie.ntu.edu.tw/~r97002/temp/Concrete%20Mathemat...)
       | actually works.
        
       | xhkkffbf wrote:
       | I became a big fan of the Monte Carlo method when I watched an
       | economist torture a problem until he found a way to turn it into
       | the heat equation, a differential equation he knew how to solve.
       | He made so many bogus assumptions that the answer was pretty much
       | worthless. But, hey, he could claim he had a closed form
       | solution!
        
         | xxxtentachyon wrote:
         | Black-Scholes uses the heat equation, is that what you're
         | thinking of?
        
       | ur-whale wrote:
       | Here's one of the most successful use of MC outside of the field
       | of nuclear physics:
       | 
       | https://graphics.stanford.edu/papers/veach_thesis/thesis.pdf
        
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       (page generated 2022-09-18 23:00 UTC)