Concentration Inequalities

Concentration inequalities are mathematical tools that describe how a random variable's values are likely to cluster around its expected or average value. They provide bounds on the probability that the variable deviates significantly from this average, helping us understand how "concentrated" or stable the outcomes are around the mean. These inequalities are useful in probability, statistics, and machine learning to assess the reliability of estimates and the likelihood of large deviations in data or algorithms.