In such simulations, we assume observed means and standard deviations are the true population parameters, while they are in fact the observed parameters of a random sample drawn from the underlying population.
Unlike other statistical methods that assume observed data to be independent, time-series analysis takes into account the stochastic dependence of consecutive observations or autocorrelation (12, 13).
So far we have assumed observed clusters are generated from complete clusters by excluding missing members, but ICS can also arise where observed clusters are complete in themselves.
In other words, we assume that observed mRNA abundance M is independent of S given that the protein is present (see Supplementary Material for discussion and detailed derivation).
Additionally, we assumed and observed little territory overlap, although generally it is reported to be 7.5% or more [67].
We assumed the observed signals have multiplicative errors except for the signals close to zero.
For instance, if an attacker is able to observe modifications, we assume that they can observe arbitrary insert, update and delete operations even if they would never occur in certain deployment scenarios, e.g., where records are never updated or deleted.
In the first method we assume that we observed only the plant phenotype, while in the second method we have additional information from the molecular marker observations.
We assume the data observed ({(X_{n},Y_{n}),ngeq1}) valued in (R^{d}times R) comes from a second-order stationary stochastic sequence.
In this paper, we assume nodes ideally observe their own context parameters and inform the system server of them via background control paths, and we consider control traffic negligible compared to the stream rate, that is, a few Mbps.
