Sentence examples for process inference from inspiring English sources

Beyond Classification -- Large-Scale Gaussian Process Inference and Uncertainty Prediction.

We introduce a scalable Kronecker approach to Gaussian process inference using the Laplace approximation, an approximate Bayesian inference method.

Because subsampling requires fewer assumptions on the nature of the data generating process, inference is less prone to a number of potential misspecification errors.

In subsequent work by Gao et al. [ 21], Gaussian process inference techniques were developed for the same model and for non-linear generalisations (Hill kinetics activation and repression models) [ 21].

To infer TF activities over time, which can be considered functional parameters in our model, we extend previously developed Gaussian process inference techniques [ 20, 21] to the case of multiple TFs interacting through a non-linear regulation function.

Data were analyzed using the Agilent Scanner and Feature Extraction software, Princeton University MicroArray database (Gollub et al. 2003), Gene Ontology Local Exploration Map (GOLEM) software (Sealfon et al. 2006), Saccharomyces Genome Database (SGD 2010), and bioPIXIE (biological Process Inference from eXperimental Interaction Evidence) software (Myers et al. 2005).

These data and the process inferences they allow will provide for significant improvements in detection limits for a widely applied geological dating and tracing technique.

We interpret multimetric slowflow response from a top-down perspective, learning from data, in order to draw dominant process inferences from observed slowflow.

Keywords from the first explanation stage: cooperative learning, conversation (+1), foundational support, creating object, hypodermic needle, extraction, inspiration, creative commons, dialoge, local community, disconnect, no time, synthesis, peer production, question/need, commons, organic process, inferences, conflict, pedagogy.

Multimetric fingerprinting offers a consistent framework for interpreting urban slowflow response, constrained by the equifinality of single slowflow metrics and the inherent limitations on process inferences that can be drawn from gauged streamflow alone.

But why should we believe that this is how listeners always process inferences?