The key techniques include two aspects: an intensity distribution calculation to obtain the probability distribution of fluorophores and a modified model selection to add a procedure that selects the initial positions of fluorophores based on the intensity distribution.
Because of the tools we use for model selection, to integrate interactions into our modeling approach, we would have to add interactive parameters for each pair of environmental and developmental parameter, which would considerably inflate our number of parameters (from about 70 to several thousand).
We centred and scaled our variables before ANCOVA analyses and performed a Bayesian information criterion (BIC) model averaging model selection to find the best model.
To investigate this possibility, we used a linear regression model with a BIC model selection to evaluate which genomic features should be included in a model predicting gbM.
In this study, we propose to use concepts related to Bayesian model selection to identify this balance.
Well, a nice aspect of this framework is that you can actually use Bayesian model selection to choose the parameters and the covariance function.
We used generalized linear model selection to test hypotheses about sea otters' spatial relationship to resources and heterospecifics.
This network discovery uses Bayesian model selection to identify the sparsity structure (absence of edges or connections) in a graph that best explains observed time-series.
We used information theoretical model selection to evaluate a set of log-linear second order autoregressive models, allowing for spatially correlated process errors.
We then used AIC model selection to compare the three major functional response models (Types I, II, and III) of ungulate browsing in relation to forage density.
We study the question of how to represent or summarize raw laboratory data taken from an electronic health record (EHR) using parametric model selection to reduce or cope with biases induced through clinical care.
