Data Processing Inequality (DPI) is a fundamental principle in information theory that states that processing data cannot increase the information content, meaning the mutual information between the input and output of a system cannot exceed the mutual information between the input and any intermediate stage. This principle underscores the idea that information can only be lost, not gained, through a series of transformations or noisy channels.

Data Processing Inequality

fundamental principle in information theory

processing data

information content

Mutual Information quantifies the amount of information obtained about one random variable through the other, capturing the dependency between them. It is a fundamental concept in information theory used to measure the reduction in uncertainty of one variable given knowledge of another, and is crucial for applications like feature selection and clustering in machine learning.

CUSTOMIZE YOUR LEARNING

CUSTOMIZE YOUR LEARNING

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