Sentence examples for models with context from inspiring English sources

A framework for log-linear models with context specific independence structures, i.e. conditional independencies holding only for specific values of the conditioning variables is introduced.

In this context, the work also presented opportunities in which emerging technologies such as RFID, which carry a high potential as tracking data sources, would significantly enhance the modeling efforts by provisioning the models with context-specific empirical inputs as close to real time as possible.

We also demonstrated that simpler models might not have any potential to improve recognition results, while the use of models with context-dependent backoff paths resulted in an accuracy improvement that was shown to be statistically significant.

Using the phylogeny-based maximum likelihood inference framework, we applied codon substitution models with context-dependent parameters to measure the mutagenic and selective processes affecting CpG dinucleotides within exonic sequence.

However, not much work has been focused on high-level event modeling with context information, such as spatio-temporal information.

A contextual goal model extends a goal model with context annotations in order to specify the variation points that are context-dependent.

Figure 4 Sequence diagram model with context-awareness information and process variables to ubiquitously perform output results.

Estimates (mean and accompanying 95% credibility interval) for the sixteen sets of base frequencies at the ancestral root sequence under the context-dependent model with context-dependent model frequencies.

Figure 9 shows (log) Bayes Factors for our context-dependent model with context-dependent model frequencies and Markov chains of different orders at the ancestral root sequence (see also Table S5 in Additional file 1).

We have calculated (log) Bayes Factors for our context-dependent model with context-dependent model frequencies and Markov chains of different orders at the ancestral root sequence in the same way as reported above, with the general time-reversible model as the reference model.

We provide additional information on this approach in Additional file 1. Figure 6 (a) shows a comparison of the substitution patterns obtained using our context-dependent model with context-dependent model frequencies, with both a first-order Markov chain and the first-order successive approximation approach to model the ancestral root sequence.