Sentence examples for models inference from inspiring English sources

We summarize the full algorithm as follows: Inference of structure and dynamic polynomial models Inference algorithms using a discrete modeling framework, such as Boolean or certain Bayesian methods, face an additional challenge: their performance depends on the choice of a data discretization method.

The value of models, inference, and findings presented using a stale data repositories rapidly depreciate with time [5].

In this paper we discuss continuous-time ARMA models, their basic properties, their relationship with discrete-time ARMA models, inference based on observations made at discrete times and non-linear processes which include continuous-time analogues of Tong's threshold ARMA models.

Details on the model specifications are provided in Additional file 1. Bayesian models' inference was made by using Markov Chain Monte Carlo (MCMC) simulations on the software R, version 2.9.2 via the library R2WinBUGS [ 30], which connects with the software WinBUGS [ 31].

Despite the simplicity of these models, inference is computationally intensive given the high number of regressors; expression data on 1000 10 000s of genes is typical depending on the experimental conditions and organism, and potentially greater if gene models (RNA splicing/transcripts) are distinguished.

As is the case for animal models, inferences in this approach thus involve excluding or removing genetic effects.

This chapter introduces concepts of the superpopulation model, model-based inference, and model-design based (model-assisted) inferences.

Nevertheless, in what follows, we address the model inference procedure for BGS-NMF with LSM prior.

Some experiments are reported to evaluate the performance of model inference and signal reconstruction.

Fig. 5 TreeMix model inference to evaluate the population splits and mixtures among the populations.

We address some derivations for model inference of BGS-NMF parameters and hyperparameters.