The phrase "a finite sample of" is correct and usable in written English.
It can be used in contexts related to statistics, research, or data analysis when referring to a limited number of observations or data points.
Example: "In our study, we collected a finite sample of responses to analyze the trends in consumer behavior."
Alternatives: "a limited sample of" or "a restricted sample of".
We took in a finite sample of 100 individuals from a population.
For economic inspection of geometric errors on freeform surfaces, only a finite sample of points could be measured across the surface.
In a discrete approximation, individual-based aspatial models can then simulate the development of a finite sample of trees, as in the traditional distance-independent individual-tree growth and yield models (Burkhart and Tomé [2012]; Weiskittel et al. [2011]).
Let us denote by Ξ a finite sample of points in D, which shall serve as surrogates for D in the calculation of errors (and error bounds) over the parameter domain.
For a fixed expiry date, we assume that a finite sample of observations ({(K_{i}, C_{i}): K_{i} in[K_{min}, K_{max}], i = 1 n}) are available in the market, where the ordered pair ((K_{i}, C_{i})) represents the price (C_{i}) corresponding to the European call option of strike price (K_{i}) contingent on a single underlying asset.
which is the tent map for b=0 and the Bernoulli map for b=1. Figure 1 shows the e-tanh map for three values of r, r=0.1,3,8 as well as a finite sample of an orbit for each value of r. Figure 2 illustrates the o-tanh map and its orbits for the same values of r.
These include errors in edge length estimation (because of a finite sampling of sites), the unpredictability of fluctuating evolutionary rates, and coalescent time.
In practice the time-integrals in (18) are realized by a finite sampling of the time interval.
A spectrum is represented as a finite sampling of an independent, identically distributed (in time) random process defined on a finite outcome space D, where the elements of D correspond to the m/ z data points established by the acquisition hardware.
Instead of representing the measured events as a finite sampling of the reference distribution, we will consider detection by calculating the probability that M reference events could be contained in the measured events and focus on the decrease in probability as M increases.
Using this technique, it is possible, within the constraints of a finite sample, to design an ideal frequency-selective filter that will preserve all elements within a specified range of frequencies and that will remove all elements outside it.
