Who is markov chain

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Message space exploration. Origin of Markov chains. Markov chain exploration. A mathematical theory of communication. Markov text exploration. The search for extraterrestrial intelligence. Current timeTotal duration Google Classroom Facebook Twitter. Video transcript Voiceover: When observing the natural world, many of us notice a somewhat beautiful dichotomy.

No two things are ever exactly alike, but they all seem to follow some underlying form. Plato believed that the true forms of the universe were hidden from us. Through observation of the natural world, we could merely acquire approximate knowledge of them.

They were hidden blueprints. The pure forms were only accessible through abstract reasoning of philosophy and mathematics. For example, the circle he describes as that which has the distance from its circumference to its center everywhere equal. Yet we will never find a material manifestation of a perfect circle or a perfectly straight line. Though interestingly, Plato speculated that after an uncountable number of years, the universe will reach an ideal state, returning to its perfect form.

This Platonic focus on abstract pure forms remained popular for centuries. It wasn't until the 16th century when people tried to embrace the messy variation in the real world and apply mathematics to tease out underlying patterns. Bernoulli refined the idea of expectation. Therefore, every day in our simulation will have a fifty percent chance of rain. Did you notice how the above sequence doesn't look quite like the original? The second sequence seems to jump around, while the first one the real data seems to have a "stickyness".

In the real data, if it's sunny S one day, then the next day is also much more likely to be sunny. We can minic this "stickyness" with a two-state Markov chain.

When the Markov chain is in state "R", it has a 0. Likewise, "S" state has 0. In the hands of metereologists, ecologists, computer scientists, financial engineers and other people who need to model big phenomena, Markov chains can get to be quite large and powerful. For example, the algorithm Google uses to determine the order of search results, called PageRank , is a type of Markov chain. Above, we've included a Markov chain "playground", where you can make your own Markov chains by messing around with a transition matrix.

Here's a few to work from as an example: ex1 , ex2 , ex3 or generate one randomly.