Model-based methods

Model-based method for OSD sizing from segmented Bscan images... 

Mobility control, issues in, 18 626 Mobility control agents polyacrylamides as, 18 625 in polymer flooding, 18 622 Mobility control surfactants, in enhanced oil recovery, 18 625-628 Mobilizable vectors, for genetic manipulation, 12 471 Mobilization, of ascorbic acid, 25 771 Modacryhc fibers, 9 192 11 188, 189, 190 dyesite content of, 11 195 flame resistance of, 11 214 flammability of, 11 194 pigmented, 11 213 U.S. production of, 11 220t Mode conversion phenomenon, 17 422 Model agreements, 24 373-374 Model-based methods, for reliability, 26 1044... 

Several approaches have been proposed to deal with population stratification by using unlinked markers. In general, these methods fall into two categories model-based and non-model-based approaches. We briefly describe and explain three major methods and discuss their advantages as well as disadvantages. The basic understanding of these model-based and non-model-based methods is necessary and helpful when users apply them to analyze genetic data. 

Compared with non-model-based methods, model-based approaches work better when no adjustment is needed or negative confounding exists. However, success of the algorithm depends on accurate subgroup classification. If random markers are used, then a large number of markers are required for the subgroup classification. 

In addition to these three major methods mentioned, several other computational approaches can also be used to deal with population stratification. For example, ADMIXMAP (22-26) is a model-based method that estimates the individual history of admixture. It can be applied to an admixed population with two or more ancestral populations. It also tests the association of a trait with ancestry at a marker locus with control for population structure. Wu et al. developed a software package in R (PSMIX) for the inference of population stratification and admixture (27). PSMIX is based on the maximum likelihood method. It performs as well as model-based methods such as STRUCTURE and is more computationally efficient. 

Recently, a hidden Markov model-based method was developed to identify LOH from tumor samples alone, taking into account SNP intermarker distances, SNP-specific heterozygosity rates, and the haplofype structure of the human genome (31) to filter out false-positive LOH. When both parents share the same haplofype, the children will inherit a long stretch of homozygous geneotypes (32). 

Figure 4 Schematic of the model-based optimization process, where performance depends on two variables (Vi and V2). Model-based methods would explore the entire oval domain, seeking the global best. Common OVAT practices only explore a few points along orthogonal trajectories. Abbreviation OVAT, one variable at a time.

In the last two decades, the researchers interest has been focused mainly on quantitative model-based methods, based on the concept of analytical or functional redundancy, which use a mathematical model of the process to obtain the estimates of a set of variables characterizing the behavior of the monitored system. The inconsistencies between estimated and measured variables provide a set of residuals,... 

V. Venkatasubramanian, R. Rengaswamy, and S.N. Kavuri. A review of process fault detection and diagnosis part II Qualitative models and search strategies quantitative model-based methods. Computers and Chemical Engineering, 27 313-326, 2003. 

From another viewpoint, LFER methods tend to be model based. Model-based methods employ sets of descriptors that often (1) model classical chemical concepts, (2) are small in number, and (3) use simple regression analyses. For example, the Flammett equation involving the logarithm of the rate constant as a linear function of the substituent constant, a (mentioned earlier), is model based. Similarly, some QSAR and QSPR studies may be viewed in this manner, and so they are included as LFER subsets in this chapter. 

Table 2 Some QM Descriptors Developed for Model-Based Methods ...

And model-based methods which are composed of quantitative model-based methods (such as analytical redundancy (Chow and Willsky, 1984), parity space (Gertler and Singer, 1990), state estimation (Willsky, 1976), or fault detection filter (Franck, 1990)) and qualitative model-based methods (such as causal methods digraphs (Shih and Lee, 1995), or fault tree (Venkatasubramanian, et ah, 2003)). 

In contrast, plant-wide optimization involves a large number of variables throughout a slowly responding plant. For this situation, a model-based method is required to approach the best conditions within reasonable times. A fundamental process model provides information on the effects of optimization variables on the profit and potential inequality constraints over a wide range of conditions. A model-based approach is required for the following situations. 

New models and ideas can be tested by computers to see how they compare with experimental results. Furthermore, calculations can be done using models, applying them to new aspects of the problem. Model-based methods have been applied in various ways to many current problems—drug design, protein folding, the struaure and funaion of biological molecular and molecular assemblies, and the effect of solution on chemical structures and... 

There are two main model-based methods for extrapolating outside the domain, steepest ascent (first-order model) and optimum path (second-order). In addition, we will briefly describe the well-known sequential simplex methods, which are model-independent. 

Similar to the difference between the Ukelihood-based haplotype inference methods and coalescent model-based methods, the above approach does not take similarity among haplot5q)es into account. It seems reasonable to assume that if a gene is associated with disease, similar haplotypes may have similar effects on disease... 

Costs related to power supply interruption is a part of the economical risk - and hence NPV calculations are a methods also here. Model based methods for estimation of expected rehabUity and interruption conditions may provide input data for the NPV calculations. 

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