Concentration Inequalities for Random Variables

Concentration inequalities are mathematical tools that estimate how likely it is for a random variable to deviate significantly from its average or expected value. They help quantify the probability that a random outcome will be far from what we anticipate, even when dealing with uncertain or random data. These inequalities are useful in fields like statistics, computer science, and machine learning to assess the reliability and stability of results, showing that with high probability, the outcomes stay close to the expected value, ensuring confidence in predictions and decisions based on random experiments.