Classical hierarchical models

J Uncertainty Analysis Using Classical and Bayesian Hierarchical Models... 

In many instances, data are intended to be associated with a natural structural representation. A typical hierarchical structure is that of a classic organization. A more general representation than a hierarchy, or tree, is known as a network. It is often possible to represent a logical data model with a hierarchical data structure. In a hierarchical model, we have a number of nodes that are connected by links. All links are directed to point from child to parent, and the basic operation in a hierarchy is that of searching a tree to find items of value. When a query is posed with a hierarchical database, all branches of the hierarchy are searched and those nodes that meet the conditions posed in the query are noted and then returned to the DBMS system user in the form of a report. 

In the classic model, the discretion afforded middle managers is limited, and the leadership they are expected to display is confined to the scope of their more-or-less hierarchical jobs. Be a good company man and implement that s the job. 

Abstract The aggregation behaviour of biomimetic polypeptide hybrid copolymers and copolypeptides is here reviewed with a particular eye on the occurrence of secondary structure effects. Structure elements like a-helix or / -sheet can induce a deviation from the classical phase behaviour and promote the formation of vesicles or hierarchical superstructures with ordering in the length-scale of microns. Polypeptide copolymers are therefore considered as models to study self-assembly processes in biological systems. In addition, they offer a great potential for a production of novel advanced materials and colloids. 

Biopharmaceutical research often involves the collection of repeated measures on experimental units (such as patients or healthy volunteers) in the form of longitudinal data and/or multilevel hierarchical data. Responses collected on the same experimental unit are typically correlated and, as a result, classical modeling methods that assume independent observations do not lead to valid inferences. Mixed effects models, which allow some or all of the parameters to vary with experimental unit through the inclusion of random effects, can flexibly account for the within-unit correlation often observed with repeated measures and provide proper inference. This chapter discusses the use of mixed effects models to analyze biopharmaceutical data, more specihcally pharmacokinetic (PK) and pharmacodynamic (PD) data. Different types of PK and PD data are considered to illustrate the use of the three most important classes of mixed effects models linear, nonlinear, and generalized linear. 

Multi-scale modeling provides a hierarchical computational approach to describe macroscopic catalytic processes. In this approach, atomistic methods (first principle quantum chemistry calculations and classical molecular dynamics) are used which reveal microscopic insight into the meehanisms and molecular-scale dynamics of reactions at electrode surfaces (for e.g. see [15,... 

The model of market and corporate behaviour which is often assumed to underlie state regulation of economic life is the neo-classical one derived from economics. According to this view regulation is the natural opponent of the market. So in the absence of regulation it is assumed that there would be market competition, and that the market without regulation would be unrestrained (Abolafia, 1985 313 Colebatch, 1989). Regulation may therefore be seen as intervention to restrain competition either to make the market operate more efficiently (i.e. remedy market failure) or to prevent undesirable outcomes (i.e. welfare objectives). This model assumes that organizations are coherent, hierarchical, and... 

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