The mechanistic approach involved applying biophysical models to predict regions where egg development and above-ground activity are possible.
Cao and Fei-Fei [14] used Latent Dirichlet Allocation (LDA) topic models to perform region level segmentation and classification, forcing the pixels within a homogeneous region to share the same latent topic.
In principle QRNA works with three probabilistic models to detect CODING regions, RNA regions, and the NULL hypothesis model.
Bedrosian and Love (2015) have demonstrated that such broad applications of these models to geologically complex regions do not remain consistent when evaluated against EarthScope impedances.
Therefore, the development of models to precisely identify regions at risk to become tularaemia hotspots will be of high priority.
However, applying the same model to all regions of interest is advisable.
Honduras's failure highlights the importance of carefully tailoring the model to specific regions and choosing more resilient assets, the authors say.
To keep a reasonable size for the model space, we limited each model to 4 regions and placed constraints on the possible circuitry of plausible DCM models.
The PhastCons program uses a Hidden Markov Model to classify regions of multiple sequence alignments into two states: conserved or neutrally evolving.
We extrapolated these models to the entire region, re-exponentiated data, and mapped residuals from the "true" population data.
