Inference for Diffusion Processes
With Applications in Life Sciences
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.
Introduction.- Stochastic Modelling in Life Sciences.- Stochastic Differential Equations and Diffusions in a Nutshell.- Approximation of Markov Jump Processes by Diffusions.- Diffusion Models in Life Sciences.- Parametric Inference for Discretely-observed Diffusions.- Bayesian Inference for Diffusions with Low-frequency Observations.- Application I: Spread of Influenza.- Application II: Analysis of Molecular Binding.- Conclusion and Outlook.- Benchmark Models.- Miscellaneous.- Supplementary Material for Application I.- Supplementary Material for Application II.- Notation.- References.
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)