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<h1 class="title toc-ignore">Some theory, and some references</h1>
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<p>I don’t intend to give full results in these notes. In shall refer to some books and papers as much as I can, without any pretension of giving th emost up to date references.</p>
<div id="coding" class="section level2">
<h2>Coding</h2>
<p><!-- - __R:__ there are many nice refferences to R. I would definitely recommend the following websites. --></p>
<p><!-- - __Matlab:__ Cleve Moler's notes are a gem in the field. But if you just want to put your hands directly on something, just take a look at the examples in ... --></p>
<p><!-- - __Python:__ as a script language, Python is much easier to learn than C or Java. If you want to do Machine Learning then, that's the perfect choice (not the same if you do Molecular Dynamics though: many people in the field of material sciences, engineers included, are still coding in Fortran). --></p>
<p><!-- - __Sage:__ Sage is a symbolic computations language. Believe it or not, it is used by many pure mathematicians working in algebra and algebraic geometry! It has some private software counterparts, like Mathematica. I like it because it is good and, overall, it is free :) --></p>
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<h2>Markov chains and stochastic simulations</h2>
<!-- Maybe the best reference for Markov chains is the classical book by Norris: -->
<!-- - Markov Chains, -->
<!-- With regards to stochastic simulations, one can refer to old books by Ross and ... -->
<!-- For Monte Carlo methods, there are many possibilities. These methods are used a lot by physicists, and they are the biggest developers of these techniques. -->
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