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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
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.