3,464 research outputs found
Multi-level agent-based modeling - A literature survey
During last decade, multi-level agent-based modeling has received significant
and dramatically increasing interest. In this article we present a
comprehensive and structured review of literature on the subject. We present
the main theoretical contributions and application domains of this concept,
with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic
statistics updated. v7 Change of the name of the paper to reflect what it
became, many refs and text added, bibliographic statistics update
Multi-level agent-based modeling with the Influence Reaction principle
This paper deals with the specification and the implementation of multi-level
agent-based models, using a formal model, IRM4MLS (an Influence Reaction Model
for Multi-Level Simulation), based on the Influence Reaction principle.
Proposed examples illustrate forms of top-down control in (multi-level)
multi-agent based-simulations
Parametric versus nonparametric: the fitness coefficient
The fitness coefficient, introduced in this paper, results from a competition
between parametric and nonparametric density estimators within the likelihood
of the data. As illustrated on several real datasets, the fitness coefficient
generally agrees with p-values but is easier to compute and interpret. Namely,
the fitness coefficient can be interpreted as the proportion of data coming
from the parametric model. Moreover, the fitness coefficient can be used to
build a semiparamteric compromise which improves inference over the parametric
and nonparametric approaches. From a theoretical perspective, the fitness
coefficient is shown to converge in probability to one if the model is true and
to zero if the model is false. From a practical perspective, the utility of the
fitness coefficient is illustrated on real and simulated datasets
Regression modeling for digital test of ΣΔ modulators
The cost of Analogue and Mixed-Signal circuit
testing is an important bottleneck in the industry, due to timeconsuming
verification of specifications that require state-ofthe-
art Automatic Test Equipment. In this paper, we apply
the concept of Alternate Test to achieve digital testing of
converters. By training an ensemble of regression models that
maps simple digital defect-oriented signatures onto Signal to
Noise and Distortion Ratio (SNDR), an average error of 1:7%
is achieved. Beyond the inference of functional metrics, we show
that the approach can provide interesting diagnosis information.Ministerio de Educación y Ciencia TEC2007-68072/MICJunta de Andalucía TIC 5386, CT 30
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