**Hoeffding’s inequality** is useful for bounding quantities that are hard to compute. It gives an upper bound on the probability that the sum of a set of random variables deviates from the expected value.

In layman’s terms, lets say we have a set of variables. When we find the average, we should usually get a result that’s close to the expected value. Hoeffding’s inequality formally states what this “close” and “usually” should be [1].

Although the inequality is a general result in probability theory, it is widely used in machine learning as well as more esoteric topics such as information theory, random algorithm analysis, and statistical learning theory.

## Hoeffding’s inequality definition

There are several equivalent forms of Hoeffding’s inequality. One common one is:

Suppose that random variables *X*_{1}, … , *X*_{n} are independent. In addition, *a*_{i} ≤ *X*_{i} ≤ *b*_{i}, and E[*X*_{i}] = µ. Then, for any t > 0,

Where

When a ≤ Xi ≤ b the formula becomes [2]:

## Relationship to Other Inequalities

Hoeffding’s inequality is a special case of the Azuma–Hoeffding inequality, a basic *concentration inequality* (inequalities that bound probabilities of deviations by a random variable from its mean or median) where the deviation probabilities decay exponentially or superexponentially — faster than exponentially — in distance from the mean.

It is also a special case of McDiarmid’s inequality, which also shows how the values of a bounded function of independent random variables are concentrated about the mean. However, while Hoeffding’s focuses on sums of random variables, McDiarmid’s inequalitycan be extended to more general functions [3].

It is similar to the Chernoff bound, but tends to be less sharp, in particular when the variance of the random variables is small.[2] It is similar to, but incomparable with, one of Bernstein’s inequalities.

## References

[1] Donke, J. Learning Theory. Statistical Machine Learning.

[2] Wasserman, L. Hoeffding’s inequality. Retrieved July 27, 2022 from: https://www.stat.cmu.edu/~larry/=stat700/Lecture6.pdf

[3] Concentration of Measure.