Sandsynlighedsregning og statistik

This textbook provides an easy-to-understand introduction to the mathematical concepts and algorithms at the foundation of data science. It covers essential parts of data organization, descriptive and inferential statistics, probability theory, and machine learning. These topics are presented in a clear and mathematical sound way to help readers gain a deep and fundamental understanding. Numerous application examples based on real data are included. The book is well-suited for lecturers and students at technical universities, and offers a good introduction and overview for people who are new to the subject. Basic mathematical knowledge of calculus and linear algebra is required.

One of the most important tasks of market research is to read market developments in such a way that one's own company can use them for its own purposes. Companies that fail to sound out the market quickly fall behind. To prevent this, panel data is being consulted in more and more industries. This book shows students and practitioners how to use panels to conduct market and product analyses. Among others, the book covers the following types of panels: retail, consumer, media, pharmaceutical, and agriculture. Readers can learn how to identify, extract, and analyze important information such as consumer buying behavior, market efforts of competitors, and general trends and developments in the market. The goal is for the reader to be able to structure marketing strategies according to the movements in the market.

An inquiry into probabilistic modes of sensing and making sense of reality developed by avant-garde artists Konrad Wojnowski argues that the probabilistic revolution, while recognized and investigated by historians of science, has been largely overlooked in the field of art. He shows that the idea that one can perceive and comprehend reality in terms of shifting probabilities was clearly present in the work of many avant-garde artists working in Europe and North America. Exploring the probabilistic aspects of the avant-garde allows him to establish a dialogue between scientific and artistic forms of knowledge. This is particularly important now, as we become surrounded by probabilistic AIs and while the very nature of cognition is being reinterpreted as inherently probabilistic. Konrad Wojnowski is Assistant Professor of Performativity Studies at Jagiellonian University, Krakow, Poland.

Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling, Computation, and Inference, Second Edition integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling, analysis and forecasting, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and contacts research frontiers in multivariate time series modeling and forecasting.It presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. It explores the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian formulations and computation, including use of computations based on Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. It illustrates the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, environmental science, and finance.Along with core models and methods, the book represents state-of-the art approaches to analysis and forecasting in challenging time series problems. It also demonstrates the growth of time series analysis into new application areas in recent years, and contacts recent and relevant modeling developments and research challenges.New in the second edition:Expanded on aspects of core model theory and methodology.Multiple new examples and exercises.Detailed development of dynamic factor models.Updated discussion and connections with recent and current research frontiers.