Sentence examples for a latent function from inspiring English sources

Hence, enzyme recruitment can be facilitated by any mutation that amplifies the cellular concentration of a catalyst to an extent such that a latent function rises to a physiologically relevant level.

For a given training dataset with observed G-quadruplexes, 𝒟={ x n, t n } n =1 N, the task is to infer a latent function f : x→ t, mapping from a G-quadruplex input x to its melting temperature t.

A flexible, non-parametric regression Gaussian Process Model (herein inferring a nonparametric latent function over time) was fitted to the mean at each time point in order to model the trend underlying mean value changes over time for each mediator.

"[Animal crackers have] a clear latent function to socialize children with ideologies of human dominance and reiterate that they have full access to the natural world and the subordinates who live in it," explains Corey Lee Wrenn, a lecturer in sociology at Monmouth University and author of A Rational Approach to Animal Rights (2015).

However, the modelling framework that we propose can still be applied by removing the translation equations and modelling the TF protein activity as a driving latent function; see [ 21] for examples of this approach to TF activity inference.

The validation error e(M i )∀i∈{1,…,241}, when plotted versus the number of dimensions, suggests the existence of a hypothetical smooth latent function between the two variables (See Figure 5).

At the core of each of these models were a set of latent function variables used to model the relationships between the data points.

A latent regression function models relationships between the data points and inference proceeds by (i) placing a Gaussian prior over the latent function, (ii) finding the optimal covariance parameters for the data, (iii) computing the posterior function distribution and integrating out the latent function to produce probabilistic predictions.

Class predictions must sum to one, thus for binary classification it is sufficient to learn the latent function for a single class (i.e.e).e

Gaussian processes allow marginalizing the latent function to obtain a marginal likelihood.

A Gaussian process prior with a linear covariance was then applied to the latent function and the posterior predictive distribution from this model was computed by applying the rules of probability calculus and integrating out the latent variables.