Gengler et al. [ 14, 15] have proposed a straightforward and quick method to predict genotype probabilities and gene contents for bi-allelic markers using a mixed model methodology, where gene content is the number of positive (negative) alleles (i.e. 2, 1, 0 for AA, Aa, aa).
DP provided the statistical analyses and modelling methodology.
These modelling methodologies thus complement the process knowledge included in white-box models with predictions based on data in areas where the white-box model assumptions are not valid or where white-box models do not provide accurate predictions.
Two studies used modelling methodologies [ 35, 50].
This paper presents a new methodology where the model outputs are corrected explicitly for the mismatch such that, with the updated parameter estimates the identification and optimization objectives are properly reconciled.
In the proposed methodology, where the model is built on the sensor level and only the coefficients are projected, in order to implement conditional Granger causality, again 10,001 models must be built at the sensor level.
We mathematically formulated the problem using the state-space methodology, where the latent time-series was modeled as an ARMA p,q) driven by innovations correlated in time.
Model-based steganography (MB) [5] introduces a different methodology, where the message is embedded in the cover according to a model representing cover message statistics.
Such an approach is greatly facilitated by adopting an ensemble methodology, where H is built as a linear combination of nonlinear vector autoregressive base models defined over overlapping subsets of genes (e.g. subnetworks).
The implications for the second part of the methodology, where it is proposed to post-process the data using a more detailed particle model, are discussed critically.
