Inference for Diffusion Processes PDF
Christiane FuchsDiffusion 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.
Inference for Diffusion Processes | SpringerLink 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
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Inference for diffusion processes with the use of `Guided Proposals` - mmider/BridgeSDEInference.jl Inference for Diffusion Processes in Apple Books
Likelihood based inference for discretely observed diffusion processes. Although partial differential equations (PDEs) with singular initial conditions such as the Kolmogorov forward equation which govern the transitional densities of diffusion processes present various difficulties from a computational perspective, the notion of a continuously evolving probability density function means that
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…