The phrase "a vector of observations" is correct and usable in written English.
It can be used in contexts related to statistics, data analysis, or research where you are referring to a collection of data points or measurements.
Example: "In our study, we collected a vector of observations to analyze the impact of temperature on plant growth."
Alternatives: "a set of observations" or "a collection of data points."
This decision technique relates a vector of observations (varvec{o}) to the strength of the hypothesis that the observed object belongs to the class (s_j), using two factors.
For each model (lambda _{k}^{(c)}), as in the discrete case, we define a vector of observations, U (k,c).
Given a vector of observations, the Viterbi decoding algorithm is applied to obtain a sequence of HMM states.
To define the form of this estimator, denote as a vector of observations and as the common location parameter of the observations.
Therefore, every response plan RP is a collection of external shock events (vec {SE}) and a vector of observations on those (vec {se}_{o}).
First, sequences from cluster k that belong to class c, (O_{r}^{(c)}), are "unrolled" to form a vector of observations U (k,c) of length (N_{k}^{(c)}T).
In such situations, an encoder in each sensor quantizes a vector of observation variables (without exchanging any information with other sensors) and transmits its output to a central processor which jointly decodes all the sources.
In the following, α = [ β, a, G, R], y obs is a vector of all observations (Gaussian and binary), bin is a vector of the binary observations only, and λ is a vector of the liabilities associated with all binary traits.
A bootstrap sample, b, is an artificial vector of observations.
where y is a vector of individual tree observations for a trait, b is a vector of fixed effects, u is a vector of random effects, e is a vector of random residuals, and X and Z correspond to design matrices relating the observations in y to the fixed and random effects in b and u, respectively.
A null model without any SNP effect is, (1) where y is a vector of N phenotypic observations, µ is the overall mean, 1 N is a vector of N ones and e is a vector of residuals which are assumed to be normally distributed with mean zero and variance.
