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Springer Simulation and Inference for Stochastic Differential Equations

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Description

This Springer Series in Statistics text provides a comprehensive look at simulation and inference for stochastic differential equations. It is designed for professionals and students in finance, engineering, and computational biology who require practical methods for modeling and estimation. The book features accessible introductory material on stochastic differential equations to help readers build a strong foundation before moving into advanced topics. What sets this work apart is its specific concentration on inference, a focus not found in other recent texts on the subject. By prioritizing the practical implementation of simulation and estimation methods, this book serves as a functional guide for real-world applications. With included R examples, readers can bridge the gap between theoretical mathematics and computational practice.

Key Features

This Springer Series in Statistics book focuses on the practical implementation of simulation and estimation methods.

The text includes R examples to assist with the computational application of stochastic differential equations.

Readers can find accessible introductory material on simulation and stochastic differential equations within the text.

The content is specifically designed for applications in the fields of finance, engineering, and computational biology.

This volume distinguishes itself from other texts through its unique concentration on the topic of inference.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
01 December 2010
Listed Since
11 September 2010

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