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Many of you will know about Markov chains. Named after Andrey Markov, [they] are mathematical systems that hop from one "state" (a situation or set of values) to another. For example, if you made a Markov chain model of a baby's behavior, you might include "playing," "eating", "sleeping," and "crying" as states, which together with other behaviors could form a 'state space': a list of all possible states. In addition, on top of the state space, a Markov chain tells you the probability of hopping, or "transitioning," from one state to any other state---e.g., the chance that a baby currently playing will fall asleep in the next five minutes without crying first.
Victor Powell and Lewis Lehe have produced a 'visual explanation' of how to produce Markov chains showing how they are used in a variety of disciplines; they are useful to computer scientists and engineers and many others. As they point out:
In the hands of meteorologists, ecologists, computer scientists, financial engineers and other people who need to model big phenomena, Markov chains can get to be quite large and powerful.
If you've not seen Markov chains in use before, or perhaps your knowledge is just a little rusty, then take a look at the link and see it they can be of any use to you.
(Score: 2) by pnkwarhall on Sunday March 01 2015, @10:41PM
Too bad it's not "news".
I would have like to see a couple of links to interesting and **recent** uses of markov chains.
Lift Yr Skinny Fists Like Antennas to Heaven
(Score: 4, Informative) by francois.barbier on Monday March 02 2015, @12:00AM
It doesn't have to be news. Remember our motto? News for nerds, stuffs that... Ow! Sh*t! I beg you pardon.
Anyway, I like this kind of algorithm visualization. It's not always easy to grasp a complex concept from text only.
Sometimes, having such visualization helps greatly in understanding the underlaying code.
For example, here is a video of 15 sorting algorithm [youtube.com].
Another one I like particularly: Amit's though on pathfinding.
He first described the A* algorithm with plain images and lot of text [stanford.edu]. Very interesting read.
Recently, he rewrote his articles and used dynamic animations [redblobgames.com] where you can move the origin, destination, walls, etc. and see how the code reacts in real time, with all the debugging information shown.
Really helps you make sense of what is going on. His other articles are all equally interesting (hexagonal grids, procedural map generation, etc.)