Structural equation modeling was used to generate latent variables of pain-related distress and pain intensity from measures used to identify patients at high risk (fear-avoidance beliefs, depression, anxiety, and catastrophizing thoughts).
It turns out that majority of the above-mentioned models still generate latent topics of inferior quality.
It is empirically found that HDLA model with three layers of hidden units is good enough to generate latent topic-based image representation having more discriminative power and retrieval accuracy than the existing topic modeling schemes.
By adding the mechanism-based, quantitative, nonlinear protein-interaction network, we can generate latent variables that integrate both gene expression and mechanistic information.
We therefore used a constrained technique to model the data, by generating latent variables orthogonal to the vintage by oCPLS2-DA.
To model dependencies among many genes, sparse factor loading matrices are used to generate latent factor models (in other words, we make the sparsity assumption that few genes among the tens of thousands assayed will reflect the underlying biology).
Systemic delivery of cytokines has several limitations, although these can be overcome by targeting their activation to sites of disease through protein engineering to generate latent cytokines [ 10, 11] or by building immunocytokines [ 12, 13].
Indeed, PLS-DA could include the variation related to the vintage in the calculation of the latent space producing models where both "terroir" and vintage confound their effects while oCPLS2-DA is able to generate latent components where the effects of vintage are excluded.
We generated latent outcomes using the following model: Y i * = η i + ϵ i where Y i * is a latent outcome for ith patient, η i is the linear predictor (as in equation 5), and ϵ i is a random error term that follows a logistic distribution with mean 0 and variance π/3.
A motion symbol generates a latent state, and a latent state generates a word.
