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In probability theory, a Chernoff bound is an exponentially decreasing upper bound on the tail of a random variable based on its moment generating function. The minimum of all such exponential bounds forms the Chernoff or Chernoff-Cramér bound , which may decay faster than exponential (e.g. sub-Gaussian ).
Chernoff bound [ edit ] The probability that a Poisson binomial distribution gets large, can be bounded using its moment generating function as follows (valid when s ≥ μ {\displaystyle s\geq \mu } and for any t > 0 {\displaystyle t>0} ):
The classical Chernoff bounds concern the sum of independent, nonnegative, and uniformly bounded random variables. In the matrix setting, the analogous theorem concerns a sum of positive-semidefinite random matrices subjected to a uniform eigenvalue bound.
The Chernoff bound of the Q-function is () ... As in the one dimensional case, there is no simple analytical formula for the Q-function. Nevertheless, ...
In his paper, Chernoff characterized the distribution through an analytic representation through the heat equation with suitable boundary conditions. Initial attempts at approximating Chernoff's distribution via solving the heat equation, however, did not achieve satisfactory precision due to the nature of the boundary conditions. [ 5 ]
In probability theory and statistics, the moment-generating function of a real-valued random variable is an alternative specification of its probability distribution.Thus, it provides the basis of an alternative route to analytical results compared with working directly with probability density functions or cumulative distribution functions.
The notation in the formula below differs from the previous formulas in two respects: [26] Firstly, z x has a slightly different interpretation in the formula below: it has its ordinary meaning of 'the x th quantile of the standard normal distribution', rather than being a shorthand for 'the (1 − x ) th quantile'.
The proof of Hoeffding's inequality follows similarly to concentration inequalities like Chernoff bounds. [9] The main difference is the use of Hoeffding's Lemma : Suppose X is a real random variable such that X ∈ [ a , b ] {\displaystyle X\in \left[a,b\right]} almost surely .