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In the project COST 207 [19], several wideband propagation models were proposed for the practical realization of both hardware and software simulators in the context of GSM systems for different classes of environments (an other set of models is ITU-R models for third-generation cellular systems).
The sire model with homogeneous residual variance (HOM) and the sire model with classes of environments (CLASS) overestimated all genetic variance components.
The use of classes of environments to account for the heterogeneous residual variance (CLASS) slightly reduced the bias of the genetic correlation between slope and intercept, but had little impact on the other genetic variance components.
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We show that in a large class of environments these distortions vanish if the principal is allowed to offer sufficiently rich contracts.
With a minor restriction on the class of environments, we demonstrate that there exists a detail-free mechanism that virtually implements competitive allocations with complete information in twice iterative dominance, irrespective of how the environment is specified.
This class of environments is very general, and it encompasses multi-armed bandits, (partially observable) Markov decision processes and others –compare Chapter 3 (Legg [2008]).
The variation in types or classes of coastal environments can be described in terms of the key variables that define them, such as climate, geomorphology, wave energy, substrate type, and, in some cases, biota, such as reefs formed by corals.
We consider the class of Bayesian environments with independent types, and utility functions which are both quasi-linear in a private good and linear in a one-dimensional private-value type parameter.
It is important to notice that the method proposed in [12] was developed for modeling complex acoustic reflections in a specific class of 3D environments obtained as the cartesian product between a 2D floor plan and a 1D (vertical) direction.
Conversational agents (also known as interactive agents and pedagogical agents) are a general class of learning environments that are either scripted or intelligently adaptive (Atkinson 2002; Craig et al. 2002; Johnson et al. 2000; Graesser and McNamara 2010; Moreno et al. 2001).
Normalized draining potentials can be classified according to the relative vertical flowpath-distances to the nearest drainages, defining classes of soil water environments.
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