The phrase "a vector of data" is correct and usable in written English.
It can be used in contexts related to data analysis, programming, or mathematics, where you are referring to a one-dimensional array or list of data points.
Example: "The algorithm processes a vector of data to identify patterns and trends."
Alternatives: "a data array" or "a data list".
We assume a vector of data (mathbf{y}= y_{k})), where (y_{k}) is the number of individuals reporting k social contacts when surveyed.
Let us assume that our symbol mapper produces a vector of data symbols d from some finite alphabet A N, where N is the frame (vector) length.
For Equation (2), assume g = H f in absence of noise where g ∈ ℂM × 1is a vector of data, H∈ ℂM × Na matrix whose elements can be considered as an over-complete dictionary as its columns and f∈ ℂN × 1the corresponding linear coefficients.
The key idea of AC model based FDIA is to find a vector of malicious data that makes the objective function after attack falls below the threshold as J({varvec{overset{lower0.5emhbox{$smash{scriptscriptstylefrown}$}}{x }}}_{bad} ) < {{tau}} (9).
More formally suppose we have a vector of discrete data Y, that contains observed values Y obs and missing values Y mis.
Assume that the count a and b are generated according to Poisson distributions with mean π at a and π bt b, respectively (2) Let x = (a,b,t a, t b) be a vector of observed data.
Here, the count of cases, which we wished to predict, was entered as a vector of missing data which would be populated with predicted case estimates following prediction modelling.
Given a column vector of data (Z=left( Z_{1},ldots,Z_{n}right) ^{prime } ), where (^{prime }) denotes transpose, let (S=left( S_{1},ldots,S_{n}right) ^{prime }) and (N=left( N_{1},ldots,N_{n}right) ^{prime } ) denote the unobserved uncorrelated components of a decomposition (Z=S+N).
For a vector of normally distributed data, this threshold corresponds to approximately 5% of the data.
We estimated the following regression equation: where X is a vector of source variables (data collected using Palm units); Weather is a vector of weather variables; β, δ, and κ are regression coefficients; and ɛ is an error term.
The ionospheric tomography problem can then be expressed mathematically by a set of simple linear algebraic equations, such as (2) where Y is a vector of the observed TEC data, x is a vector of the unknown electron densities, and A is the geometry matrix that describes the relationship between the line-of-sight TEC data and the electron densities.
