Models/modeling model-based inference

With the view that a KBS interpreter is a method for mapping from input data in the form of intermediate symbolic state descriptions to labels of interest, four families of approaches are described here, each offering inference mechanisms and related knowledge representations that can be used to solve interpretation problems namely, model-based approaches, digraphs, fault trees, and tables. These methods have been heavily used... 

Although the existence or absence of a particular process can often be determined from observed data, an assessment of how well an algorithm represents the process is often difficult to make due to observation errors, natural variations in field data, and lack of sufficient data on individual component processes. In such circumstances, model validity must be inferred or possibly based on comparisons with laboratory data obtained under controlled conditions. Often laboratory data provide the basis for developing an algorithm since field data are so much more difficult and expensive to collect and interpret. Examples of system representation errors and their analysis were presented at the Pellston workshop (6 ). 

Mossbauer spectroscopy involves the measurement of minute frequency shifts in the resonant gamma-ray absorption cross-section of a target nucleus (most commonly Fe occasionally Sn, Au, and a few others) embedded in a solid material. Because Mossbauer spectroscopy directly probes the chemical properties of the target nucleus, it is ideally suited to studies of complex materials and Fe-poor solid solutions. Mossbauer studies are commonly used to infer properties like oxidation states and coordination number at the site occupied by the target atom (Flawthome 1988). Mossbauer-based fractionation models are based on an extension of Equations (4) and (5) (Bigeleisen and Mayer 1947), which relate a to either sums of squares of vibrational frequencies or a sum of force constants. In the Polyakov (1997)... 

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. 

Bayesian statistics are applicable to analyzing uncertainty in all phases of a risk assessment. Bayesian or probabilistic induction provides a quantitative way to estimate the plausibility of a proposed causality model (Howson and Urbach 1989), including the causal (conceptual) models central to chemical risk assessment (Newman and Evans 2002). Bayesian inductive methods quantify the plausibility of a conceptual model based on existing data and can accommodate a process of data augmentation (or pooling) until sufficient belief (or disbelief) has been accumulated about the proposed cause-effect model. Once a plausible conceptual model is defined, Bayesian methods can quantify uncertainties in parameter estimation or model predictions (predictive inferences). Relevant methods can be found in numerous textbooks, e.g., Carlin and Louis (2000) and Gelman et al. (1997). 

When thermodynamics or physics relates secondary measurements to product quality, it is easy to use secondary measurements to infer the effects of process disturbances upon product quality. When such a relation does not exist, however, one needs a solid knowledge of process operation to infer product quality from secondary measurements. This knowledge can be codified as a process model relating secondary to primary measurements. These strategies are within the domain of model-based control Dynamic Matrix Control (DMC), Model Algorithmic Control (MAC), Internal Model Control (IMC), and Model Predictive Control (MPC—perhaps the broadest of model-based control strategies). 

Paleoelevation models based on fossil floras use three different approaches 1) the use of floras to estimate temperature, which is used in combination with lapse rates to infer elevation 2) the use of floras to estimate enthalpy, which is used with gravitational acceleration to estimate elevation and 3) the use of stomatal frequency in leaves to indicate altitudinal changes in C02 partial pressure. This paper will focus on the first of these, the temperature-lapse rate method, which itself has three basically different approaches that differ in the way paleotemperatures can be estimated from fossil floras and in the methods by which lapse rates can be utilized in the calculations. The purpose of this paper is to provide a concise overview that summarizes... 

Fig. 3. Sensor filtered signals and inferred model-based substrate concentration for test 1 (A) and test 2 (B) (A) biomass ( ) ethanol ( ) substrate).

Fatumo S, Plaimas K, Adebiyi E et al (2011) Comparing metabolic network models based on genomic and automatically inferred enzyme information from Plasmodium and its human host to define drug targets in silico. Infect Genet Evol 11 708-715... 

Sa1 excitation, which generates little bare molecule emission, fast IVR for the state pumped is also observed. While concrete proof for a serial mechanism is yet to come in the form of rise and fall times for intermediate states, the inference of this mechanism is quite strong in the results. The discussion below for anilinefNj) clusters, and the simple two parameter serial IVR/VP model based on Fermi s Golden Rule and RRKM theory, will provide the final demonstration for this mechanism. 

The concentration of the product B, CB, is not measured on-line and a measurement is only available hourly from a lab. The control of the concentration is therefore based on inferential control in loop 4 using the reactor temperature T. The inferential controller will then, from a model of the process, infer what the concentration CB is and use this inferred measurement as the signal to the controller CC3 (where the first C refers to Concentration). 

In these case studies, in addition to a brief discussion of the catalytic applications, representative reactions are discussed with the aim of illustrating in detail the relationships between surface structures (as inferred from investigations with probe molecules) and catalytic activity. The following topics are discussed in detail (i) MgO as a model catalyst for base-catalyzed reactions (ii) the mechanism of ethene hydrogenation on ZnO (iii) Cu20 as an oxidation catalyst for the conversion of methanol to formaldehyde, with... 

A set of assumptions and inference options upon which a model is based, including underlying theory as well as specific functional relationships. 

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