Sentence examples for latent observations from inspiring English sources

Geostatistical models take into account spatial correlation by introducing location-specific random effects as latent observations from a multivariate spatial Gaussian process (37).

Treating missing cases as latent observations has been proposed, but requires the grouping of cases in successive generations rather than on a temporal basis [ 27].

The latent observations U i (1 ) introduced at each location s i are assumed to be derived from a multivariate normal distribution with a covariance matrix Σ n x n (1 ), i.e. U (1 ) ~ MVN (0, Σ n x n (1 ) ).

To enable model fit we approximate the spatial process by a subset of locations, knots, { s i *, i=1,…, m} (m<<n) with latent observations U *(1 )=(U s1 *),…, U sm *)) T. U *(1 ) is considered to arise from the same Gaussian process as U (1 ) and thus U *(1 )~ N 0,Σ*), where Σ* is the mxm covariance matrix of the sub-process.

These latent observations U of the original process can be approximated by the 'predictions' of the sub-process via the mean of Gaussian conditional distribution U (1) (s ) ∣ U * (1) ~ N (Q T Σ * −1 U * (1), σ 2 - Q T Σ * - 1 Q ), that is U ˆ = Q T Σ * - 1 U * (1), where Q= Cov(U * (1), U (1) ) is an mxn matrix of the covariance functions between the full and the sub-process (48, 49).

I think the poignancy of the article comes from the sense of the crushing inevitability of technology and the latent observation that we as a species hurtle forward, enthusiastically accepting all new forms of technology and in the process giving up/losing something of the past — something elegiac about this.

I think the poignancy of the article comes from the sense of the crushing inevitability of technology and the latent observation that we as a species hurtle forward, enthusiastically accepting all new forms of technology and in the process giving up/losing something of the past—something elegiac about this.

Because most OAV items were positively skewed (mean  = 1.25, range  = −0.56 to 4.32) and kurtotic (mean  = 1.27, range  = 1.64 to 19.23) and because our data set contained non-independent observations, latent factor models (CFA, ESEM, and MIMIC) were fitted by using the Robust Maximum Likelihood (MLR) estimator in combination with the "Complex" option in Mplus.

*Represents residual correlation of latent responses for observations within each applicant.

In the absence of a gold standard for the clinical observations, a latent class analysis (LCA) evaluating the true within-herd lameness prevalence was used.

One typical approach to underdetermined BSS is the statistical modeling of observations using latent variables [3, 4].