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While not derived as a Riemann sum, taking the average of the left and right Riemann sums is the trapezoidal rule and gives a trapezoidal sum. It is one of the simplest of a very general way of approximating integrals using weighted averages. This is followed in complexity by Simpson's rule and Newton–Cotes formulas.
First note that the definition of an equidistributed sequence is equivalent to the integral criterion whenever f is the indicator function of an interval: If f = 1 [c, d], then the left hand side is the proportion of points of the sequence falling in the interval [c, d], and the right hand side is exactly .
The Poisson summation formula is also useful to bound the errors obtained when an integral is approximated by a (Riemann) sum. Consider an approximation of S ( 0 ) = ∫ − ∞ ∞ d x s ( x ) {\textstyle S(0)=\int _{-\infty }^{\infty }dx\,s(x)} as δ ∑ n = − ∞ ∞ s ( n δ ) {\textstyle \delta \sum _{n=-\infty }^{\infty }s(n\delta ...
Siegel derived it from the Riemann–Siegel integral formula, an expression for the zeta function involving contour integrals. It is often used to compute values of the Riemann–Siegel formula, sometimes in combination with the Odlyzko–Schönhage algorithm which speeds it up considerably.
The trapezoidal rule may be viewed as the result obtained by averaging the left and right Riemann sums, and is sometimes defined this way. The integral can be even better approximated by partitioning the integration interval, applying the trapezoidal rule to each subinterval, and summing the results. In practice, this "chained" (or "composite ...
It follows from the law of large numbers that the empirical probability of success in a series of Bernoulli trials will converge to the theoretical probability. For a Bernoulli random variable , the expected value is the theoretical probability of success, and the average of n such variables (assuming they are independent and identically ...
Denjoy's probabilistic argument for the Riemann hypothesis [33] is based on the observation that if μ(x) is a random sequence of "1"s and "−1"s then, for every ε > 0, the partial sums = (the values of which are positions in a simple random walk) satisfy the bound () = (/ +) with probability 1. The Riemann hypothesis is equivalent to this ...
The probability of selection under this scheme is = where X is the sum of the N x variates and the x i are the n members of the sample. Then the ratio of the sum of the y variates and the sum of the x variates chosen in this fashion is an unbiased estimate of the ratio estimator.