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Indeed, LIDE with mixture model requires about 415 MB.

For this image, LIDE with mixture model requires memory less than one-seventh of that required by AHE.

This mixture model requires only two parameters, and it can capture the algebraic tail as well as the mode.

The implementation of this sort of mixture model requires that the data be flagged depending on type (PK (DVID = 1 where DVID is a flag defining the type of observation) or dynamic (DVID = 2)).

Like many computational methods, CENTIPEDE [ 19], based on a hierarchical Bayesian mixture model, requires position weight matrices of known TF binding motifs; therefore, its ability is dependent on the availability of TF binding motifs.

Hence, LIDE with mixture model would require roughly one seventh of memory needed by integral image based AHE.

Application of the model requires input data for the equivalent thermal conductivity of the fluid mixture in the annular space.

However, this model requires measurements of CPB temperature during the prediction periods and does not consider the effects of the mixture recipe and mechanical load (e.g., stress) and deformation (e.g., consolidation); that is, the model is empirical.

A quantitative model requires accurate data mining.

Check if your irrigation model requires this.

Effective parameter estimation for such mixture models would require substantial sample sizes thereby limiting these approaches to large, well-defined cohorts.