Difference between revisions of "The Data Processing Inequality"

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If we can write:
 
If we can write:
  
{{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=x, Y=y, Z=z\right) = P\left(Z=z\mid Y=y\right)\cdot P\left(Y=y\mid X=x\right) \cdot P\left(X=x\right)</math>|{{EquationRef|2}}}}
 +
 
 +
Or in a more compact form:
 +
 
 +
{{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|3}}}}
  
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 10:07, 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)

Or in a more compact form:

 

 

 

 

(3)


The Data Processing Inequality

Sufficient Statistics

Fano's Inequality