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"This book is meant as a guide for implementing Bayesian methods for latent variable models. I have included thorough examples in each chapter, highlighting problems that can arise during estimation, potential solutions, and guides for how to write up findings for a journal article. This book is structured into 12 main chapters, beginning with introductory chapters comprising Part I. Part II is comprised of Chapters 3-5. Each of these chapters deals with various models and techniques related to measurement models within SEM. Part III contains Chapters 6-7, on extending the structural model. Part IV contains Chapters 8-10, on longitudinal and mixture models. Finally, Part IV contains chapters that discuss special topics"--

Bayesian statistical decision theory --- Social sciences

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"Bayesian statistical analyses have become increasingly common over the last two decades. The rapid increase in computing power that facilitated their implementation coincided with major changes in the research interests of, and data availability for, social scientists. Specifically, the last two decades have seen an increase in the availability of panel data sets, other hierarchically structured data sets including spatially organized data, along with interests in life course processes and the influence of context on individual behavior and outcomes. The Bayesian approach to statistics is well-suited for these types of data and research questions. Applied Bayesian Statistics is an introduction to these methods that is geared toward social scientists. Author Scott M. Lynch makes the material accessible by emphasizing application more than theory, explaining the math in a step-by-step fashion, and demonstrating the Bayesian approach in analyses of U.S. political trends drawing on data from the General Social Survey."--

Bayesian statistical decision theory. --- Mathematical statistics. --- Probabilities. --- Markov processes.

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In this open-access-book the author concludes that expertise could be the key factor for global and interconnected problems. Experimental results have shown that expertise was a stronger predictor than public information regarding change in behavior and strategy adaption. Identifying non-routine problem-solving experts by efficient online assessments could lead to less volatile system performance, from which all decision-makers could potentially profit.

Management & management techniques --- Operational research --- Management --- Operations Research/Decision Theory --- Operations Research and Decision Theory --- Uncertainty --- Decision-making --- Interpretation --- Complexity --- Information --- Routine --- Open Access --- Management decision making

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Dieses Forschungsvorhaben zielt darauf ab, individuelle Entrepreneure hinsichtlich ihrer Veränderungstendenzen zur Nutzung der Entscheidungslogik Effectuation und Causation zu untersuchen – insbesondere während des Gründungsprozesses. Basierend auf einem qualitativen Fallstudiendesign zeigen sowohl fallinterne als auch fallübergreifende Analysen, wie Entrepreneure zunächst an der effektuativen Logik festhalten und im Gründungsverlauf zu hybriden Logikformen übergehen. Der Beitrag zur Weiterentwicklung der Forschung zielt in drei Richtungen: Erstens schließt die Arbeit Lücken in der Effectuation-Forschung, indem sie Entscheidungsfindung von Unternehmensgründern auf individueller Ebene spezifiziert. Zweitens ermöglicht die Fokussierung auf die einzelnen Gründungsphasen ein besseres Verständnis der Veränderungstendenzen einschließlich der Kausalzusammenhänge (wann, wie und warum). Insbesondere werden Erkenntnisse zur Auffälligkeit von Veränderungssprüngen von einer Phase zur anderen geliefert. Drittens kann durch die Beleuchtung der verschiedenen Subdimensionen von Effectuation und Causation ihre zunehmend hybride Verwendung im Zeitverlauf der Gründung das Verständnis für transformative Prozesse entwickeln. Die Autorin Dr. Katrin Mattes lehrt seit 2013 als Dozentin an der HTWG Konstanz und DHBW Villingen-Schwenningen und arbeitet als Unternehmensberaterin für strategische Markenführung und Marketing-Management. Ihr Forschungsschwerpunkt liegt im effektuativen Entrepreneurship.

Entrepreneurship. --- New business enterprises. --- Operations research. --- Operations Research and Decision Theory.

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Bayesian Inference of State Space Models: Kalman Filtering and Beyond offers a comprehensive introduction to Bayesian estimation and forecasting for state space models. The celebrated Kalman filter, with its numerous extensions, takes centre stage in the book. Univariate and multivariate models, linear Gaussian, non-linear and non-Gaussian models are discussed with applications to signal processing, environmetrics, economics and systems engineering. Over the past years there has been a growing literature on Bayesian inference of state space models, focusing on multivariate models as well as on non-linear and non-Gaussian models. The availability of time series data in many fields of science and industry on the one hand, and the development of low-cost computational capabilities on the other, have resulted in a wealth of statistical methods aimed at parameter estimation and forecasting. This book brings together many of these methods, presenting an accessible and comprehensive introduction to state space models. A number of data sets from different disciplines are used to illustrate the methods and show how they are applied in practice. The R package BTSA, created for the book, includes many of the algorithms and examples presented. The book is essentially self-contained and includes a chapter summarising the prerequisites in undergraduate linear algebra, probability and statistics. An up-to-date and complete account of state space methods, illustrated by real-life data sets and R code, this textbook will appeal to a wide range of students and scientists, notably in the disciplines of statistics, systems engineering, signal processing, data science, finance and econometrics. With numerous exercises in each chapter, and prerequisite knowledge conveniently recalled, it is suitable for upper undergraduate and graduate courses.

Statistical science --- statistiek --- systeemtheorie --- Kalman filtering. --- Bayesian statistical decision theory --- Filtre de Kalman --- Estadística bayesiana --- Data processing.