Various methods based on latent variable modelling have been proposed for this situation.
Three families of multi-variable models have been developed and identified on the grounds of experimental step and PRBS tests.
In other disciplines, such as cardiology or clinical trials, such multi-variable models have been invaluable in modeling the heterogeneity of risk and treatment benefits and can be used to guide therapy and improve outcomes [ 8- 12].
The result shows that the developed two input variable model has provided excellent estimations for Madurai and also other six Indian climatic locations of latitudes ranging from 9°N to 13°N.
Overall, the variable model has 2-fold characteristics: 1) if mutation patterns belong to Classes I and II, the variable model has a unique equilibrium regardless of initial GC content values and 2) if mutation patterns belong to Class III, the variable model can have plural equilibria.
Also, a Bayesian hidden variable model has been developed to study the occurrence of foodborne pathogens in the pork production chain [ 18].
The process was initiated using the best bivariate model (identified above; average C = 0.614), and iteratively repeated until the performance of very large p-variable models had been evaluated.
The model in E (where E = V 2.ρ) recorded the best apparent performance amongst single-variable models and would have been selected as the best single-variable model had results been based solely on the calibration dataset with no subsequent validation.
