Sentence examples for one observed data from inspiring English sources

More generally, systems models will require simulation to obtain a trace of behaviour which can then be compared with the data, or we can approximate the likelihood of the model connecting one observed data point with the next, as we now discuss.

3) Although the exact dates and figures we have taken for first treatment probabilities prior to the observation period may not apply to the whole country, the values assumed seem plausible as we obtained an increasing sequence of probabilities from 1982 up to the first one estimated based on observed data in 1991, which happens to have a similar slope to that estimated in the observed period.

Only one phase was observed (data not shown), and the linear fit of the concentration dependence gave a second-order rate constant of 313 μM−1 s−1.

Simulated daily outcome counts were generated for each scenario with average daily counts corresponding to those in the one year of observed data; with the specified temporal associations with air pollutants; and with associations with variables relating to time and meteorology (and other pollutants in two-pollutant models) that reflected the associations in the one year of observed data.

After setting up the model parameters and designing the appropriate block discretization, we performed the 3-D inversions for two sets of observed data; one set includes the full MT impedance tensor and another set contains only off-diagonal elements of the MT impedance.

It also has a lower complexity because for each observed data, one has only to update a weighted average [27].

Each point in the plane thus represents the departure from the long term mean of one calendar year's observed data.

Here L- is the likelihood of a gene being absent in all taxa while L+ is the likelihood of the gene present in at least one genome from the observed data.

20 As in the presentation of the results for sensitivity scenarios, we present only the observed data, one variant of MI (Scheme A), and the related three datasets for sensitivity analysis.

This differs from MCAR in that it assumes that the missing data are dependent on one or more variables in the observed data.

Sparse model calibration allows for dynamic subspace identification by adaptively searching through a large combination of possible relevant subspaces to identify an appropriate one that best explains the observed data.