Random Process Analysis With R (Hardcover)

Marco Bittelli

ISBN 10: 0198862512 / ISBN 13: 9780198862512

Editore: Oxford University Press, Oxford, 2022

Riguardo questo articolo

Hardcover. Random process analysis (RPA) is used as a mathematical model in physics, chemistry, biology, computer science, information theory, economics, environmental science, and many other disciplines. Over time, it has become more and more important for the provision of computer code and data sets. This book presents the key concepts, theory, and computer code written in R, helping readers with limited initial knowledge of random processes to become confident in their understanding and application of these principles in their own research. Consistent with modern trends in university education, the authors make readers active learners with hands-on computer experiments in R code directing them through RPA methods and helping them understand the underlying logic.Each subject is illustrated with real data collected in experiments performed by the authors or taken from key literature. As a result, the reader can promptly apply the analysis to their own data, making this book an invaluable resource for undergraduate and graduate students, as well as professionals, in physics, engineering, biophysical and environmental sciences, economics, and social sciences. This book presents the key concepts, theory, and computer code written in R, helping readers with limited initial knowledge of random processes to become confident in their understanding and application of these principles in their own research. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Codice articolo 9780198862512

Dati bibliografici

• Titolo: Random Process Analysis With R (Hardcover)
• Editore: Oxford University Press, Oxford
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 0198862512
• ISBN 13: 9780198862512
• Rilegatura: Hardcover
• Condizione: new

Descrizione articolo

Riassunto

Random process analysis (RPA) is used as a mathematical model in physics, chemistry, biology, computer science, information theory, economics, environmental science, and many other disciplines. Over time, it has become more and more important for the provision of computer code and data sets. This book presents the key concepts, theory, and computer code written in R, helping readers with limited initial knowledge of random processes to become confident in their understanding and application of these principles in their own research. Consistent with modern trends in university education, the authors make readers active learners with hands-on computer experiments in R code directing them through RPA methods and helping them understand the underlying logic.

Each subject is illustrated with real data collected in experiments performed by the authors or taken from key literature. As a result, the reader can promptly apply the analysis to their own data, making this book an invaluable resource for undergraduate and graduate students, as well as professionals, in physics, engineering, biophysical and environmental sciences, economics, and social sciences.

Informazioni sull?autore

Marco Bittelli received a degree in Agricultural Sciences from the University of Bologna, Italy, in 1994 and an M.S. and a Ph.D. in Soil Physics from Washington State University, USA, in 2001. He teaches Soil and Environmental Physics, Statistics and Philosophy of Science courses at the University of Bologna.


Roberto Olmi received a degree in Physics from the University of Firenze, Italy, in 1983. Since 1984 he has been a researcher at the Institute of Research on Electromagnetic Waves of the National Research Council in Firenze.


Rodolfo Rosa received a degree in Physics in 1968 and in Philosophy in 1977 from the University of Bologna. From 1969 to 1992, he was a researcher at the National Research Council-Institute of Microelectronics and Microsystems, Bologna. From 1992 to 2014 he was a Professor at the Faculty of Statistics at the University of Bologna, where he taught courses on Statistics for Experimental Research, Chaos and Complexity, and Probability and Stochastic Processes.