Sentence examples for models where given from inspiring English sources

Notice that in Eq. 2, (sum _{j=1}^{N_{g}}tilde {G}_{ij,g}y_{j,g}) is identical to the i t h row of (tilde {boldsymbol {G}_{g}}boldsymbol {y}_{g}), which appears in Eq. 1. Consumer demand models where, given prices, an individual's demand increases with average demand of some reference group also generate a similar specification (see Gaertner 1974; Pollak 1976; Alessie and Kapteyn 1991; Case 1991).

We work under a Gaussian noise model where, given the qi's, the hi's are centered about class means μ± with class standard deviations σ±, all of which must be inferred from the data.

We must therefore also determine the error with models where a given function is dependent on multiple loci, including those that are spatially non-adjacent.

A replacement probability was calculated for each individual variable by summing the normalised pseudo-likelihoods for the models where the given variable was replaced.

We tested the one-sided hypotheses that parameter estimates are larger than 0 with likelihood ratio tests, by comparing models where a given parameter was set to 0 with a model where all parameters were allowed to assume non-negative values [see [ 59] for details of this model and associated example script].

We tested the one-sided hypotheses that parameter estimates are larger than 0 with likelihood ratio tests, by comparing models where a given parameter was set to 0 with a model where all parameters were allowed to assume non-negative values [ 46].

A useful approximation of this process is the ClonalOrigin model (Didelot et al. 2010), where given the clonal genealogy the recombinant lines of ancestry are assumed to be independent of each other.

We face a similar challenge in the second phase of the approach, where, given the mixture model and the sample partition probability vector, we search for modulator genes based on the correlation of their expression vectors with.

The effects of adjusting for the leverages, or not, are similar to the effects of using REML instead of ML to fit mixed linear models, where ML gives biased variance component estimates and the estimates are more sensitive to data imbalance [ 12].

This is comparable to YCombinator's model, where they give $6,000 per founder and take 6-86-8% the startups equity.

"People are just redoing the old model, where you give 5percentt a year.