We present a general method for characterizing uncertainty in model choice that is tailored to reprioritizing SNPs within a hit region under strong LD.
This paper shows that there is a strong interdependence between ambiguity aversion and the preferences for the timing of the resolution of uncertainty, as dened by the classic work of Kreps and Porteus (1978): the modeling choices that are being made in the domain of ambiguity aversion influence the set of modeling choices available in the domain of timing attitudes.
The ThreeWay R-package presents the user with modeling choices that have to be made during the analysis process, such as the number of components to be estimated per mode.
We note that statistical inferences based on modelling choices that were determined by the outcome of statistical tests, such as ours, are open to question and critique and partly for this reason we present our methods only in the context of sensitivity or secondary analyses.
This exact population size constraint is an arbitrary model choice that we have relaxed to simplify the algebra.
For example, formal ontological analysis can be applied to specify a conceptual model, verify its consistency and identify modelling choices that potentially lead to faulty results; or formal ontology can be applied to formally specify the meaning of terms in a vocabulary (e.g. to enable communication between autonomous intelligent agents).
I introduce a new approximate-Bayesian method for comparative phylogeographical model-choice that estimates the temporal distribution of divergences across taxa from multi-locus DNA sequence data.
We fit an econometric model to choices that dissociated risk attitudes, loss aversion, and choice consistency using hierarchical Bayesian techniques to both pool data and allow heterogeneity in decision-making.
The design of computational models is path-dependent: the choices made in each step during model development constrain the choices that are available in the subsequent steps.
All the existing multiple testing procedures have been developed without considering a proper model choice, so that, as Lee and Bjørnstad [ 4] showed, existing methods may not maintain the stated FDR level if any of their model assumptions are wrong.
