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This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time.
The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.
Die ästhetische Tonalität des Jazz : Immersion durch 3D-Visualisierung und musikalische Interaktion
(2021)
Das Ausstellungskonzept »Free Jazz: A Collective Improvisation« widmet sich der ästhetischen Tonalität des gleichnamigen Albums von Ornette Coleman. Das besagte Album bildete den Grundstein für die Etablierung des Free Jazz – einer Musikform, die sich durch ihre schrille, hierarchiefreie Klangwelt sowie ein freies Improvisationsspiel auszeichnet und mit den gängigen Konventionen des Jazz bricht. Zu jedem der 8 Musiker, welche gemeinsam das von Coleman zusammengestellte Doppelquartett ergeben, wurden individuell abgestimmte, animierte und interaktive 3D-Gebilde konzipiert. Diese reagieren auf den Sound der jeweiligen Instrumente sowie die Position der Besuchenden und ermöglichen einen audiovisuellen, immersiven Zugang zum Album. Die Interaktion zwischen dem Publikum und den Gebilden schafft Raum für eine kollektive Improvisation auf sowohl der visuellen als auch auditiven Ebene.
The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.
The progress in natural language processing (NLP) research over the last years, offers novel business opportunities for companies, as automated user interaction or improved data analysis. Building sophisticated NLP applications requires dealing with modern machine learning (ML) technologies, which impedes enterprises from establishing successful NLP projects. Our experience in applied NLP research projects shows that the continuous integration of research prototypes in production-like environments with quality assurance builds trust in the software and shows convenience and usefulness regarding the business goal. We introduce STAMP 4 NLP as an iterative and incremental process model for developing NLP applications. With STAMP 4 NLP, we merge software engineering principles with best practices from data science. Instantiating our process model allows efficiently creating prototypes by utilizing templates, conventions, and implementations, enabling developers and data scientists to focus on the business goals. Due to our iterative-incremental approach, businesses can deploy an enhanced version of the prototype to their software environment after every iteration, maximizing potential business value and trust early and avoiding the cost of successful yet never deployed experiments.
Dieser "Crashkurs" eignet sich ausgezeichnet für die kompakte Wiederholung und die zielgerichtete Prüfungsvorbereitung. Das Buch ist aufgrund seiner fallbezogenen Ausrichtung vor allem für Anfänger gedacht, eignet sich aber auch für fortgeschrittene Studierende zur kompakten Wiederholung. Einfache Merksätze, Fälle, Übersichten, Definitionen und kurze Zusammenfassungen lassen sich leicht einprägen und geben Sicherheit für die Prüfung. Vorteile auf einen Blick: Das wichtigste BGB-Know-how als Repetitorium vor der Prüfung, mit erprobten Merksätzen und kurzen Zusammenfassungen,
Fall für Fall sicher durch die Prüfung
The fourth industrial revolution introduces disruptive technologies to production environments. One of these technologies are multi-agent systems (MASs), where agents virtualize machines. However, the agent's actual performances in production environments can hardly be estimated as most research has been focusing on isolated projects and specific scenarios. We address this gap by implementing a highly connected and configurable reference model with quantifiable key performance indicators (KPIs) for production scheduling and routing in single-piece workflows. Furthermore, we propose an algorithm to optimize the search of extrema in highly connected distributed systems. The benefits, limits, and drawbacks of MASs and their performances are evaluated extensively by event-based simulations against the introduced model, which acts as a benchmark. Even though the performance of the proposed MAS is, on average, slightly lower than the reference system, the increased flexibility allows it to find new solutions and deliver improved factory-planning outcomes. Our MAS shows an emerging behavior by using flexible production techniques to correct errors and compensate for bottlenecks. This increased flexibility offers substantial improvement potential. The general model in this paper allows the transfer of the results to estimate real systems or other models.