Sentence examples for sparse probabilistic from inspiring English sources

We discuss the implications of our results in Section 4. Our approach to isoform deconvolution from RNA-Seq data consists of fitting a sparse probabilistic model, like several existing methods including rQuant (Bohnert and Rätsch, 2010), NSMAP (Xia et al., 2011), IsoLasso (Li et al., 2011b), SLIDE (Li et al., 2011a) or iReckon (Mezlini et al., 2013).

For example, the probabilistic sparse matrix factorization approach uses the 'sparse' constraint in the matrix decomposition to provide a combinatorial account of the gene expression in terms of a small set of factors.

Next, we develop a constrained probabilistic sparse matrix factorization (cPSMF) approach that models the expression of each gene across the independent latent components as a linear weighted combination of activity profiles of a small number of TFs.

By incorporating TF-target gene relationships derived from ChIP-chip data into the probabilistic sparse matrix factorization, the cPSMF approach infers the network structure in a more accurate and robust manner.

Because grounding accidents are rare events and data is sparse, their analysis requires a probabilistic approach.

Bayesian sparse learning is performed to conduct probabilistic reconstruction based on the relevant group bases.

Using the Hodgkin–Huxley (H-H) model and Stochastic Collocation on Sparse Grids, we obtain an accurate probabilistic interpretation of the deterministic dynamics of the transmembrane potential and gating variables.

Belief propagation [see (Meltzer et al., 2009) review and (Pearl, 1988) textbook] is an inference method for probabilistic graphical networks with sparse interdependence or locality.

The proposed two-scale framework combines a random domain decomposition (RDD) and a probabilistic collocation method (PCM) on sparse grids to quantify these two sources of uncertainty, respectively.

Relevance vector machine (RVM), pioneered by Tipping, is a sparse Bayesian learning algorithm for regression and probabilistic classification developed from the standard SVM [31, 32].

Basically, the superiority of BGS-NMF-LSM to other NMFs is three-fold, i.e. Bayesian probabilistic modeling, group basis representation and sparse reconstruction weight.