Authors:
- Explicit instructions for diffusion modelling enable practitioners to apply this powerful class of processes
- Both stochastic modelling and statistical inference for diffusion processes are comprehensively covered in one book
- Explains in detail a Bayesian approach which enables parameter estimation for diffusion models in many applications in life sciences
- Graphical illustrations facilitate the understanding of Bayesian imputation techniques and associated convergence considerations
- Methods are illustrated on complex real data applications from epidemic modelling and fluorescence microscopy
- Required knowledge on stochastic calculus is provided in a special chapter
- Includes supplementary material: sn.pub/extras
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Table of contents (10 chapters)
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Front Matter
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Statistical Inference
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Front Matter
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Applications
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Front Matter
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Back Matter
About this book
Diffusion processes are a promising instrument for realistically modelling the time-continuous evolution of phenomena not only in the natural sciences but also in finance and economics. Their mathematical theory, however, is challenging, and hence diffusion modelling is often carried out incorrectly, and the according statistical inference is considered almost exclusively by theoreticians. This book explains both topics in an illustrative way which also addresses practitioners. It provides a complete overview of the current state of research and presents important, novel insights. The theory is demonstrated using real data applications.
Reviews
From the reviews:
“The book under review is aimed at introducing both modelling and inference for diffusions and applying the statistical estimation of complex diffusion models to real data sets. It addresses to theoreticians (e.g., mathematicians and statisticians) as well as practitioners (e.g., bioinformaticians and biologists) with basic knowledge about deterministic differential equations, probability theory and statistics. … the book under review is recommended to researchers with strong background through deterministic differential equations, probability theory and statistics.” (Iris Burkholder, zbMATH, Vol. 1276, 2014)Authors and Affiliations
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, Institute for Bioinformatics and Systems, Helmholtz Zentrum München, Neuherberg, Germany
Christiane Fuchs
About the author
Christiane Fuchs received an MSc degree in Computational Mathematics from Brunel University West London in 2003 and a Diploma in Mathematics from the University of Hanover in 2005. In 2010 she completed her doctorate in Statistics at the Ludwig-Maximilians-Universität Munich.
After an interim research stay at the University of Warwick in 2010 she is currently a postdoctoral fellow at the Helmholtz Centre in Munich.
Bibliographic Information
Book Title: Inference for Diffusion Processes
Book Subtitle: With Applications in Life Sciences
Authors: Christiane Fuchs
DOI: https://doi.org/10.1007/978-3-642-25969-2
Publisher: Springer Berlin, Heidelberg
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2013
Hardcover ISBN: 978-3-642-25968-5Published: 16 January 2013
Softcover ISBN: 978-3-642-43017-6Published: 26 June 2015
eBook ISBN: 978-3-642-25969-2Published: 18 January 2013
Edition Number: 1
Number of Pages: XIX, 430
Topics: Statistical Theory and Methods, Statistics for Life Sciences, Medicine, Health Sciences, Statistics for Business, Management, Economics, Finance, Insurance, Biostatistics