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}}}} | ||
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== 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:
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(1)
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If we can write:
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(2)
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Or in a more compact form:
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(3)
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