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Background: Many different simulation frameworks, in different topics, need to treat realistic datasets to initialize and calibrate the system. A precise reproduction of initial states is extremely important to obtain reliable forecast from the model. Methodology/Principal Findings: This paper proposes an algorithm to create an artificial population where individuals are described by their age, and are gathered in households respecting a variety of statistical constraints (distribution of household types, sizes, age of household head, difference of age between partners and among parents and children). Such a population is often the initial state of microsimulation or (agent) individual-based models. To get a realistic distribution of households is often very important, because this distribution has an impact on the demographic evolution. Usual techniques from microsimulation approach cross different sources of aggregated data for generating individuals. In our case the number of combinations of different households (types, sizes, age of participants) makes it computationally difficult to use directly such methods. Hence we developed a specific algorithm to make the problem more easily tractable. Conclusions/Significance: We generate the populations of two pilot municipalities in Auvergne region (France) to illustrate the approach. The generated populations show a good agreement with the available statistical datasets (not used for the generation) and are obtained in a reasonable computational time. [ABSTRACT FROM AUTHOR]

The CMS experiment is expected to start data taking during 2008, and large data samples, of the Peta-bytes scale, will be produced each year. The CMS Physics Tools package provides the CMS physicist with a powerful and flexible software layer for analysis of these huge datasets that is well integrated in the CMS experiment software. C++ generic programming is used to allow simple extensions of analysis tools. A core part of this package is the Candidate Model providing a coherent interface to different types of data. Standard tasks such as combinatorial analyses, generic cuts, MC truth matching and constrained fitting are supported. Advanced template techniques enable the user to add missing features easily. We explain the underlying model, certain details of the implementation and present some use cases showing how the tools are currently used in generator and full simulation studies as preparation for analysis of real data. [ABSTRACT FROM AUTHOR]