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INTRODUCTION

I wrote about Claude Shannon who used Markov chains in information theory. See http://www.kinberg.net/wordpress/stellan/equations/#informationtheory

INDEX

• introduction
• IConditional probability – Markov chains
• Shannon information theory
• Conclusion

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Conditional probability

Where event probability depends on other events. So the events are dependent.

https://www.mathsisfun.com/data/probability-events-conditional.html

Can be illustrated with a Bayesian network like this:

Another good course is this:

Origins of Markov chains is explained by Khan Academy

Everything in the world is governed by precise ratios and a constant law of change.

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Shannon information theory

See http://www.kinberg.net/wordpress/stellan/equations/#informationtheory

 

I dont know why Edward Witten choose to start with a short introduction to Communication theory (the Shannon theory) at “Theoretical Physics 2018: From Qubits to Spacetime”

I presume it is important to know about this, to understand the theory of Quantum mechanics and aspects of General Relativity as he says to continue with at the conference.
I decided to take a look at C.E. Shannons book from 1948 ” A mathematical theory of Communication“

Shannon was an American mathematician, electrical engineer, and cryptographer known as “the father of information theory“. He wrote also “Theoretical Genetics.”^[12]

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conclusion

 

 Proba