Overview
Conditional entropy is a measure of the uncertainty of a random variable given another random variable. It is used to measure the amount of information that one random variable provides about the other. For example, knowing the value of one random variable allows us to make more accurate predictions about the other. Conditional entropy has applications in a wide variety of fields, including information theory, machine learning, economics, and biology. It is also used to quantify the overall uncertainty in a system, and to measure the complexity of a system. In addition, it is used to quantify the uncertainty of a prediction, which helps to optimize decisions.
Research published in this journal
No peer-reviewed research on this exact topic has been published in Model Based Research yet. Browse the journal →