distributionpreserving
Distribution preserving refers to a property of a transformation or operation that maintains the underlying probability distribution of a dataset or system. In essence, if you apply a distribution-preserving transformation to data, the new data will follow the same probability distribution as the original data, even if the specific values within the data change. This means that statistical properties like the mean, variance, and shape of the distribution remain consistent.
This concept is crucial in various fields, including statistics, machine learning, and signal processing. For instance,
In the context of random variables, a function is distribution-preserving if applying it to a random variable