We derive an extension of McDiarmid’s inequality for functions f with bounded differences on a high probability set Y (instead of almost surely). The behavior of f outside Y may be arbitrary. The proof is short and elementary, and relies on an extension argument similar to Kirszbraun’s theorem [4].
In this work we design a general method for proving moment inequalities for polynomials of independent random variables. Our method works for a wide range of random variables including Gaussian, Boolean, exponential, Poisson and many others. We apply our method to derive general concentration inequalities for polynomials of independent random variables. We show that our method implies concentration inequalities for some previously open problems, e.g. permanent of random symmetric matrices. We show that our concentration inequality is stronger than the well-known concentration inequality due to Kim and Vu [29]. The main advantage of our method in comparison with the existing ones is a wide range of random variables we can handle and bounds for previously intractable regimes of high degree polynomials and small expectations. On the negative side we show that even for boolean random variables each term in our concentration inequality is tight. Strong concentration results play a fundamental role in probabilistic combinatorics and theoretical computer science. In this paper, we present several new concentration results developed recently by the author and collaborators. To illustrate the power of these new results, we discuss applications in many different areas of mathematics, from combinatorial number theory to the theory of random graphs.  Suppose t1, ..., tn are independent random variables which take value either 0 or 1, and Y is a multi-variable polynomial in ti’s with positive coefficients. We give a condition which guarantees that Y concentrates strongly around its mean even when several variables could have a large effect on Y . Some applications will be discussed. §