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RooStats is a project to create advanced statistical tools required for the analysis of LHC data, with emphasis on discoveries, confidence intervals, and combined measurements. The idea is to provide the major statistical techniques as a set of C++ classes with coherent interfaces, so that can be used on arbitrary model and datasets in a common way. The classes are built on top of the RooFit package, which provides functionality for easily creating probability models, for analysis combinations and for digital publications of the results. We will present in detail the design and the implementation of the different statistical methods of RooStats. We will describe the various classes for interval estimation and for hypothesis test depending on different statistical techniques such as those based on the likelihood function, or on frequentists or bayesian statistics. These methods can be applied in complex problems, including cases with multiple parameters of interest and various nuisance parameters.
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The RooStats toolkit, which is distributed with the ROOT software package, provides a large collection of software tools that implement statistical methods commonly used by the High Energy Physics community. The toolkit is based on RooFit, a high-level data analysis modeling package that implements various methods of statistical data analysis. RooStats enforces a clear mapping of statistical concepts to C++ classes and methods and emphasizes the ability to easily combine analyses within and across experiments. We present an overview of the RooStats toolkit, describe some of the methods used for hypothesis testing and estimation of confidence intervals and finally discuss some of the latest developments.
A key feature of collaboration in science and software development is to have a {em log} of what and how is being done - for private use and reuse and for sharing selected parts with collaborators, which most often today are distributed geographically on an ever larger scale. Even better if this log is {em automatic}, created on the fly while a scientist or software developer is working in a habitual way, without the need for extra efforts. The {tt CAVES} and {tt CODESH} projects address this problem in a novel way, building on the concepts of {em virtual state} and {em virtual transition} to provide an automatic persistent logbook for sessions of data analysis or software development in a collaborating group. A repository of sessions can be configured dynamically to record and make available the knowledge accumulated in the course of a scientific or software endeavor. Access can be controlled to define logbooks of private sessions and sessions shared within or between collaborating groups.
The Collaborative Analysis Versioning Environment System (CAVES) project concentrates on the interactions between users performing data and/or computing intensive analyses on large data sets, as encountered in many contemporary scientific disciplines. In modern science increasingly larger groups of researchers collaborate on a given topic over extended periods of time. The logging and sharing of knowledge about how analyses are performed or how results are obtained is important throughout the lifetime of a project. Here is where virtual data concepts play a major role. The ability to seamlessly log, exchange and reproduce results and the methods, algorithms and computer programs used in obtaining them enhances in a qualitative way the level of collaboration in a group or between groups in larger organizations. The CAVES project takes a pragmatic approach in assessing the needs of a community of scientists by building series of prototypes with increasing sophistication. In extending the functionality of existing data analysis packages with virtual data capabilities these prototypes provide an easy and habitual entry point for researchers to explore virtual data concepts in real life applications and to provide valuable feedback for refining the system design. The architecture is modular based on Web, Grid and other services which can be plugged in as desired. As a proof of principle we build a first system by extending the very popular data analysis framework ROOT, widely used in high energy physics and other fields, making it virtual data enabled.
Tcl/tk provides for fast and flexible interface design but slow and cumbersome vector processing. Octave provides fast and flexible vector processing but slow and cumbersome interface design. Calling octave from tcl gives you the flexibility to do a broad range of fast numerical manipulations as part of an embedded GUI. We present a way to communicate between them.
The best linear unbiased estimator (BLUE) is a popular statistical method adopted to combine multiple measurements of the same observable taking into account individual uncertainties and their correlation. The method is unbiased by construction if the true uncertainties and their correlation are known, but it may exhibit a bias if uncertainty estimates are used in place of the true ones, in particular if those estimated uncertainties depend on measured values. This is the case for instance when contributions to the total uncertainty are known as relative uncertainties. In those cases, an iterative application of the BLUE method may reduce the bias of the combined measurement. The impact of the iterative approach compared to the standard BLUE application is studied for a wide range of possible values of uncertainties and their correlation in the case of the combination of two measurements.
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