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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 ).
Craig's formula was later extended by Behnad (2020) [5] for the Q-function of the sum of two non-negative variables, ... The Chernoff bound of the Q-function is () ...
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.
Therefore, the theorem above gives a tighter bound than the Ahlswede–Winter result. The chief contribution of (Ahlswede & Winter 2003) was the extension of the Laplace-transform method used to prove the scalar Chernoff bound (see Chernoff bound#Additive form (absolute error)) to the case of self-adjoint
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} ):
Using the fact that (,) =, the generalized Marcum Q-function can alternatively be defined as a finite integral as (,) = (+) ().However, it is preferable to have an integral representation of the Marcum Q-function such that (i) the limits of the integral are independent of the arguments of the function, (ii) and that the limits are finite, (iii) and that the integrand is a Gaussian function ...
In statistics, the Bhattacharyya distance is a quantity which represents a notion of similarity between two probability distributions. [1] It is closely related to the Bhattacharyya coefficient, which is a measure of the amount of overlap between two statistical samples or populations.
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 .