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Simulation and Inference for Stochastic Processes with Yuima: A Comprehensive R Framework for Sdes and Other Stochastic Processes

The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allows for the simulation of stochastic;

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Introduction to Stochastic Analysis

This is an introduction to stochastic integration and stochastic differential equations written in an understandable way for a wide;

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Bayesian Inference for Stochastic Processes

many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete;

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Bayesian Inference for Stochastic Processes

many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete;

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Stochastic PDEs and Dynamics

theoretical framework for random attractors. With rigorous mathematical deduction, the book is an essential reference to mathematicians and physicists;

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Statistical Inference for Fractional Diffusion Processes

economics. In model building, statistical inference for stochastic processes is of great importance from both a theoretical and an applications;

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Semimartingales and their Statistical Inference

of stochastic processes, including diffusion type processes, point processes, and diffusion type processes with jumps, widely used for stochastic;

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Semimartingales and Their Statistical Inference

of stochastic processes, including diffusion type processes, point processes, and diffusion type processes with jumps, widely used for stochastic;

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Asymptotic Analysis of Unstable Solutions of Stochastic Differential Equations

transition densities, processes that are not solutions of any Ito's SDEs, and the Bessel diffusion process. The book is self-contained, with;

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Asymptotic Analysis of Unstable Solutions of Stochastic Differential Equations

transition densities, processes that are not solutions of any Ito's SDEs, and the Bessel diffusion process. The book is self-contained, with;

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Stochastic Modelling For Systems Biology

researchers. Focusing on computer simulation, the author examines the use of stochastic processes for modelling biological systems. He provides a;

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Stochastic Processes - Inference Theory

for stochastic processes and measures, and includes recent material on Ridge regression with some unexpected applications, for example;

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Monte-carlo Methods and Stochastic Processes

focuses on the simulation of stochastic processes in continuous time and their link with partial differential equations (PDEs). It covers linear;

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Monte-Carlo Methods and Stochastic Processes

focuses on the simulation of stochastic processes in continuous time and their link with partial differential equations (PDEs). It covers linear;

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Levy Processes & Stochastic Calculus

introduction to the general theory of Levy processes, then leading on to develop the stochastic calculus for Levy processes in a direct and accessible;

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Simulation and Chaotic Behavior of Alpha-stable Stochastic Processes

Presents new computer methods in approximation, simulation, and visualization for a host of alpha-stable stochastic processes.;

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Simulation and Chaotic Behavior of Alpha-stable Stochastic Processes

Presents new computer methods in approximation, simulation, and visualization for a host of alpha-stable stochastic processes.;

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Introduction to Stochastic Processes with R

maturity, Introduction to Stochastic Processes with R features: * More than 200 examples and 600 end-of-chapter exercises * A tutorial for getting;

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Stochastic Processes for Physicists

provides a solid understanding of stochastic processes and stochastic calculus in physics, without the need for measure theory. In avoiding measure;

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Probability

it within a framework for creating models of random phenomena. The author focuses on the synthesis of stochastic models concurrent with the;

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Bayesian Analysis of Stochastic Process Models

stochastic processes, providing a unified treatment. * Provides a thorough introduction for research students. * Computational tools to deal with;

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Numerical Approximations of Stochastic Differential Equations with Non-Globally Lipschitz Continuous Coefficients

stochastic processes. Using this approach, the authors establish moment bounds for fully and partially drift-implicit Euler methods and for a class;

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Simulation and Inference for Stochastic Differential Equations

. The introductory material on simulation and stochastic differential equation is very accessible and will prove popular with many readers;

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Stochastic Processes: Modeling and Simulation

This sequel to volume 19 of Handbook on Statistics on Stochastic Processes: Modelling and Simulation is concerned mainly with the theme;

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Probability, Statistics, and Stochastic Processes

, and Stochastic Processes, Second Edition prepares readers to collect, analyze, and characterize data in their chosen fields. Beginning with;

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Optimal Statistical Inference in Financial Engineering

testing hypothesis and discriminant analysis for independent observations. It then explores stochastic processes, many famous time series models;

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Elements of Applied Stochastic Processes

statistical inference in stochastic processes. * Integration of theory and application offers improved teachability * Provides a comprehensive;

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