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Chernoff faces, invented by applied mathematician, statistician, and physicist Herman Chernoff in 1973, display multivariate data in the shape of a human face. The individual parts, such as eyes, ears, mouth, and nose represent values of the variables by their shape, size, placement, and orientation.
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).
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 ]
Such inequalities are of importance in several fields, including communication complexity (e.g., in proofs of the gap Hamming problem [13]) and graph theory. [ 14 ] An interesting anti-concentration inequality for weighted sums of independent Rademacher random variables can be obtained using the Paley–Zygmund and the Khintchine inequalities.
If (as in the setup for the Lovász local lemma) each variable depends on at most Δ others, an equitable coloring of the dependence graph may be used to partition the variables into independent subsets within which Chernoff bounds may be calculated, resulting in tighter overall bounds on the variance than if the partition were performed in a ...
The Q-function can be generalized to higher dimensions: [14] = (),where (,) follows the multivariate normal distribution with covariance and the threshold is of the form = for some positive vector > and positive constant >.
Autocorrelation plot; Bar chart; Biplot; Box plot; Bullet graph; Chernoff faces; Control chart; Fan chart; Forest plot; Funnel plot; Galbraith plot; Histogram; Mosaic ...
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 matrices. The procedure given in the derivation below. All of the recent works on this topic follow this same procedure, and ...