Efron-Stein Inequality

The Efron-Stein inequality provides a way to estimate how much a random variable varies, based on the influence of its individual parts. Imagine a complex outcome built from many independent components; this inequality helps measure how much changing each component can affect the overall result. By examining these individual contributions, it offers an upper bound on the variability of the entire system’s outcome. This is useful in statistics and machine learning for understanding how sensitive a prediction or measurement is to small changes or uncertainty in the data.