Økonometri og økonomisk statistik

Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book's practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include Matlab computations, and the numerous end-of-chapter exercises include computational assignments. Matlab code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.

This book focuses on the foundations needed to be successful in managing and engaging with data analytics initiatives, bridging the gap between creators and users of data.Currently, every company, no matter its size, is data-driven in one way or another; using data to improve customer experience, as a new value stream, and to stay competitive. However, many business leaders, professionals, and students-such as executives, business analysts, UI/UX designers, project managers and marketing teams -are forced to interact with data and those who generate data, without being taught the general competencies needed to feel comfortable having these conversations.This book focuses on the foundations needed to be successful in managing and engaging with data analytics initiatives, bridging the gap between creators and users of data. As a management reference guide, it discusses the different types of data strategy needed for succeeding with data, covering topics such as data team composition, types of data analytics, the importance of data storytelling, and identifying data ROI.Framed by the author's personal story, the trove of information is made tangible through the compelling narrative with its unprecedented accessibility and readability for a non-technical audience.If you suffer from fear of data, anxiety around conversations with technical teams, this practical approach book can help with actions you can start implementing right away.

Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models and more.