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Can a Chess Piece Explain Markov Chains? | Infinite Series

2017-01-12

[public] 173K views, 6.01K likes, 63.0 dislikes audio only

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In this episode probability mathematics and chess collide. What is the average number of steps it would take before a randomly moving knight returned to its starting square?

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Previous Episode - When Pi is Not 3.14

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Mathematician Kelsey Houston-Edwards explains Markov Chains and how they can be used to determine the probability of random chess moves, as well as the likelihood your favorite music will pop up on the radio.

Sources include:

Probability: Theory and Examples

https://www.amazon.com/Probability-Cambridge-Statistical-Probabilistic-Mathematics/dp/0521765390

Markov Chains

https://www.amazon.com/Markov-Cambridge-Statistical-Probabilistic-Mathematics/dp/0521633966

Markov Chains and Mixing Times

http://pages.uoregon.edu/dlevin/MARKOV/markovmixing.pdf

Written and Hosted by Kelsey Houston-Edwards

Produced by Rusty Ward

Graphics by Ray Lux

Made by Kornhaber Brown (www.kornhaberbrown.com)

Comments answered by Kelsey:

Taylor Kinser

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Huy Dinh

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Funky Tom

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Steve’s Mathy Stuff

/youtube/video/ineO1tIyPfM&lc=z12pcjlgey25shhxd04ch5epeojxwf2gey00k.1483698612220851


Probability Transition Function
/youtube/video/63HHmjlh794?t=127.81
General Markov Chain Theory
/youtube/video/63HHmjlh794?t=200.049
The Stationary Distribution
/youtube/video/63HHmjlh794?t=229.12
Theorem about Stationary Distributions
/youtube/video/63HHmjlh794?t=303.41
Stationary Distribution
/youtube/video/63HHmjlh794?t=386.38