By applying three different computer programs to the same image sets, we also tested the reliability of the programs with respect to subsequent geometric inferences such as Bravais lattice type assignments.
This allows for "parametric model based imaging" [2, 3] where the accuracies and precisions of extracted structural-geometric image parameters are statistically estimated on the basis of information theory (i.e., maximum likelihood, negative Boltzmann entropy [5] or maximum log-likelihood [6] methods) and geometric inferences [7] are possible.
Hypothesis tests or confidence intervals based on the geometric mean make inference to the population median, and subsequent wording indicates the median is the parameter being tested/estimated.
These results demonstrate the utility of efficient approximate inference in geometric space as a means to identify therapeutic relationships, suggesting a role of these methods in drug repurposing efforts.
Under the guiding principle of using large-scale 3D data for representation learning, the efforts described in this thesis have led to top-performing algorithms for pure-3D data processing, as well as 3D-assisted semantic, geometric and physical property inference from 2D images.
The inference engine extracts geometric data from a geometric model in CATIA V5 and processes the knowledge using the geometric data.
We also discuss techniques from geometric viewpoints for estimation and inferences as well as for assessing their statistical properties.
It examines the issues of object-based representation of code provisions Industry Foundation Class (IFC -compliant object model for buIFC -compliant; Java-based rule inference and IFC-based geobjectc reasoning; and software prototype imodelentation on the J2EE platform.
The theorems all have an "if-then" form, enabling them to authorize inferences of their consequents, given their antecedents.[30] But then so too do the problems in effect have an "if-then" logical form, for the (geometric style) solutions they provide authorize inferences from given information to unknowns.
After scaling node ages, the dates for the MRCA of Acacia thrips were 14.38 mya under the Bayesian consensus, and 25.32 mya as the geometric mean calculated from the PL inferences.
