Coursera
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École normale supérieure
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3 HN citations
In this course you will learn a whole lot of modern physics (classical and quantum) from basic computer programs that you will download, generalize, or write from ...
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Hacker News Comments about Statistical Mechanics: Algorithms and Computations
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Dec 22, 2017
kgwgk on
A Zero-Math Introduction to Markov Chain Monte Carlo Methods
You can not sample the distribution directly when “it is impossible to solve for analytically”. There are a few methods to overcome this issue, in particular “MCMC methods allow us to estimate the shape of a posterior distribution in case we can’t compute it directly”.
You only need to be able to compute the relative values of the function you want to integrate (in this case a probability density) at the current point and the proposed destination. Where is this function coming from is not relevant to understand MCMC methods, they can be applied in many problems unrelated to Bayesian statistics.
Here is another (non-zero-math) introduction to the topic:
https://arxiv.org/pdf/cond-mat/9612186.pdf
https://www.coursera.org/learn/statistical-mechanics/lecture...
Mar 14, 2017
grondilu on
Monte Carlo estimation of Pi
BTW there is a nice related course on coursera: "Statistical Mechanics: Algorithms and Computations"[1]. Also notice there is a rosettacode entry for this[2].
1.
https://www.coursera.org/learn/statistical-mechanics
2.
http://rosettacode.org/wiki/Monte_Carlo_methods
Apr 28, 2016
mrcactu5 on
Markov Chain Monte Carlo Sampling
The intrinsic important of MCMC is clear. What kinds of applications would be of excitement to startup-oriented readers here on YCombinator?
Also why is this called Quantum MCMC rather than just normal Markov Chain Monte Carlo?
By coincidence there is a Coursera Cousrse that just started on Statistical Mechanics and Algorithms, and the first exercise is to approximate pi.
https://www.coursera.org/learn/statistical-mechanics
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