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.
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
1: Introduction 2: Historical Background 3: Introduction to Stochastic Processes 4: Poisson Processes 5: Random Walk 6: ARMA Processes 7: Spectrum Analysis 8: Markov Chain Monte Carlo 9: Bayesian Inference and Stochastic Processes 10: Genetic Algorithms: an Evolutionary-based Global Random Search 11: The Problem of Accuracy 12: Spatial Analysis 13: How Random is a Random Process? Appendix A: Bootstrap Appendix B: JAGS List of symbols List of R Codes References Index
1: Introduction 2: Historical Background 3: Introduction to Stochastic Processes 4: Poisson Processes 5: Random Walk 6: ARMA Processes 7: Spectrum Analysis 8: Markov Chain Monte Carlo 9: Bayesian Inference and Stochastic Processes 10: Genetic Algorithms: an Evolutionary-based Global Random Search 11: The Problem of Accuracy 12: Spatial Analysis 13: How Random is a Random Process? Appendix A: Bootstrap Appendix B: JAGS List of symbols List of R Codes References Index
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