Difference between revisions of "The Data Processing Inequality"

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== Markovity ==
 
== Markovity ==
A Markov Chain is a random process that describes a sequence of possible events where the probability of each event depends only on the outcome of the previous event. Thus, we say that <math>X, Y, Z</math> is a Markov chain in this order, denoted as:
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A [https://en.wikipedia.org/wiki/Markov_chain Markov Chain] is a random process that describes a sequence of possible events where the probability of each event depends only on the outcome of the previous event. Thus, we say that <math>X, Y, Z</math> is a Markov chain in this order, denoted as:
  
 
{{NumBlk|::|<math>X \rightarrow Y \rightarrow Z</math>|{{EquationRef|1}}}}
 
{{NumBlk|::|<math>X \rightarrow Y \rightarrow Z</math>|{{EquationRef|1}}}}
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{{NumBlk|::|<math>P\left(x, y, z\right) = P\left(z\mid y\right)\cdot P\left(y\mid x\right) \cdot P\left(x\right)</math>|{{EquationRef|2}}}}
 
{{NumBlk|::|<math>P\left(x, y, z\right) = P\left(z\mid y\right)\cdot P\left(y\mid x\right) \cdot P\left(x\right)</math>|{{EquationRef|2}}}}
  
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Note that in the above equation, <math>P\left(x\right)</math> is just a compact way to write <math>P\left(X=x\right)</math>.
  
 
== The Data Processing Inequality ==
 
== The Data Processing Inequality ==

Revision as of 10:04, 23 October 2020

Markovity

A Markov Chain is a random process that describes a sequence of possible events where the probability of each event depends only on the outcome of the previous event. Thus, we say that is a Markov chain in this order, denoted as:

 

 

 

 

(1)

If we can write:

 

 

 

 

(2)

Note that in the above equation, is just a compact way to write .

The Data Processing Inequality

Sufficient Statistics

Fano's Inequality