消息首页搜索举报

【现货速发】扩散过程推断理论及其在生命科学中的应用(英文版)

全新正版书籍，24小时发货，可开发票。

74.2 7.5折 99 全新

库存4件

天津津南

认证卖家担保交易快速发货售后保障

作者（德）Christiane Fuchs（C.福克斯）

出版社世界图书出版公司

ISBN9787519211684

出版时间2016-07

装帧平装

开本16开

定价99元

货号23998252

上书时间2024-12-19

易安居书舍

已实名已认证进店收藏店铺

在售商品暂无
平均发货时间 14小时
好评率暂无

最新上架

【现货速发】中国书法发展史 ¥47.10

【现货速发】上海的时光容器 ¥18.30

【现货速发】平凹说小说 ¥21.00

【现货速发】阴阳的哲学 ¥62.50

【现货速发】中学生黑板报手抄报实用大全(修订版) ¥13.80

【现货速发】孩子们的视唱练耳(1) ¥23.90

【现货速发】日本战后日语教育国际化研究 ¥48.20

【现货速发】露珠 ¥28.60

【现货速发】量体钩衣按体型钩钩针毛衣 ¥16.00

商品详情

品相描述：全新

商品描述: 导语摘要
《扩散过程推断理论及其在生命科学中的应用》是一部理论和实践相结合的参考图书，是数学、统计学以及一些前沿学科生物信息学和生物学的不可或缺的学习参考用书。和已有的同类型图书相比，本书是在一个框架下处理扩散用的模型和推断，因此囊括了实际应用中所需的所有步骤，有基本的微分方程、概率论和统计知识即可学习该书。

作者简介
《扩散过程推断理论及其在生命科学中的应用》是一部理论和实践相结合的参考图书，作者为数学、统计学以及一些前沿学科生物信息学和生物学的不可或缺的学习参考用书。

目录
1 Introduction
  1.1 Aims of This Book
  1.2 Outline of This Book

Part Ⅰ Stochastic Modelling
2 Stochastic Modelling in Life Sciences
  2.1 Compartment Models
  2.2 Modelling the Spread of Infectious Diseases
    2.2.1 History of Epidemic Modelling
    2.2.2 SIR Model
    2.2.3 Model Extensions
  2.3 Modelling Processes in Molecular Biology，Biochemistry and Genetics
    2.3.1 History of Chemical Reaction Modelling
    2.3.2 Chemical Reaction Kinetics
    2.3.3 Reaction Kinetics in the Biological Sciences
  2.4 Algorithms for Simulation
    2.4.1 Simulation of Continuous-Time Markov Jump Processes
    2.4.2 Simulation of Solutions of ODEs and SDEs
  2.5 Conclusion
  References
3 Stochastic Differential Equations and Diffusions in a Nutshell
  3.1 Brownian Motion and Gaussian White Noise
    3.1.1 Brownian Motion
    3.1.2 Brownian Bridge
    3.1.3 Gaussian White Noise
    3.1.4 Excursus： Lovy Processes
  3.2 Ito Calculus
    3.2.1 Stochastic Integral and Stochastic Differential Equations
    3.2.2 Different Stochastic Integrals
    3.2.3 Existence and Uniqueness of Solutions
    3.2.4 Transition Density and Likelihood Function
    3.2.5 Ito Diffusion Processes
    3.2.6 Sample Path Properties
    3.2.7 Ergodicity
    3.2.8 Kolmogorov Forward and Backward Equations
    3.2.9 Infinitesimal Generator
    3.2.10 Ito Formula
    3.2.11 Lamperti Transformation
    3.2.12 Girsanov Formula
  3.3 Approximation and Simulation
    3.3.1 Convergence and Consistency
    3.3.2 Numerical Approximation
    3.3.3 Simulation of Brownian Bridge
  3.4 Concluding Remarks
  References
4 Approximation of Markov Jump Processes by Diffusions
  4.1 Characterisation of Processes
  4.2 Motivation and Purpose
  4.3 Approximation Methods
    4.3.1 Convergence of the Master Equation
    4.3.2 Convergence of the Infinitesimal Generator
    4.3.3 Langevin Approach
    4.3.4 Kramers-Moyal Expansion
    4.3.5 Van Kampen Expansion
    4.3.6 Other Approaches
  4.4 Extensions to Systems with Multiple Size Parameters
    4.4.1 Convergence of the Master Equation
    4.4.2 Convergence of the Infinitesimal Generator
    4.4.3 Langevin Approach
    4.4.4 Kramers-Moyal Expansion
    4.4.5 Van Kampen Expansion
  4.5 Choice of Stochastic Integral
  4.6 Discussion and Conclusion
  References
5 Diffusion Models in Life Sciences
  5.1 Standard SIR Model
    5.1.1 Model
    5.1.2 Jump Process
    5.1.3 Diffusion Approximation
    5.1.4 Summary
    5.1.5 Illustration
  5.2 Multitype SIR Model
    5.2.1 Model
    5.2.2 Jump Process
    5.2.3 Diffusion Approximation
    5.2.4 Summary
    5.2.5 Illustration and Further Remarks
  5.3 Existence and Uniqueness of Solutions
  5.4 Conclusion
  References

Part Ⅱ Statistical Inference
6 Parametric Inference for Discretely-Observed Diffusions
  6.1 Preliminaries
    6.1.1 Time-Continuous Observation
    6.1.2 Time-Discrete Observation
    6.1.3 Time Scheme
  6.2 Naive Maximum Likelihood Approach
    6.3 Approximation of the Likelihood Function
  6.3.1 Analytical Approximation of the Likelihood Function
    6.3.2 Numerical Solutions of the Kolmogorov Forward Equation
    6.3.3 Simulated Maximum Likelihood Estimation
    6.3.4 Local Linearisation
  6.4 Alternatives to Maximum Likelihood Estimation
    6.4.1 Estimating Functions
    6.4.2 Generalised Method of Moments
    6.4.3 Simulated Moments Estimation
    6.4.4 Indirect Inference
    6.4.5 Efficient Method of Moments
  6.5 Exact Algorithm
  6.6 Discussion and Conclusion
  References
7 Bayesian Inference for Diffusions with Low-Frequency Observations
  7.1 Concepts of Bayesian Data Augmentation for Diffusions
    7.1.1 Preliminaries and Notation
    7.1.2 Path Update
    7.1.3 Parameter Update
    7.1.4 Generalisation to Several Observation Times
    7.1.5 Generalisation to Several Observed Diffusion Paths
    7.1.6 Practical Concerns
    7.1.7 Example： Ornstein-Uhlenbeck Process
    7.1.8 Discussion
  7.2 Extension to Latent Data and Observation with Error
    7.2.1 Latent Data
    7.2.2 Observation with Error
  7.3 Convergence Problems
  7.4 Improvements of Convergence
    7.4.1 Changing the Factorisation of the Dominating Measure
    7.4.2 Time Change Transformations
    7.4.3 Particle Filters
    7.4.4 Innovation Scheme on Infinite-Dimensional State Spaces
  7.5 Discussion and Conclusion
  References

Part Ⅲ Applications
8 Application Ⅰ：Spread of Influenza
  8.1 Simulation Study
    8.1.1 Data
    8.1.2 Parameter Estimation
  8.2 Example： Influenza in a Boarding School
    8.2.1 Data
    8.2.2 Parameter Estimation
  8.3 Example： Influenza in Germany
    8.3.1 Data
    8.3.2 Parameter Estimation
  8.4 Conclusion and Outlook
  References
9 Application Ⅱ： Analysis of Molecular Binding
  9.1 Problem Statement
    9.1.1 Data Acquisition by Fluorescence Recovery After Photobleaching
    9.1.2 Research Questions
  9.2 Preliminary Analysis
    9.2.1 Impact of Binding
    9.2.2 Impact of Diffusion
  9.3 General Model
    9.3.1 Compartmental Description
    9.3.2 Diffusion Approximation
    9.3.3 Deterministic Approximation
    9.3.4 Simulation Study
  9.4 Refinement of the General Model
    9.4.1 Compartmental Description
    9.4.2 Diffusion Approximation
    9.4.3 Deterministic Approximation
    9.4.4 Simulation Study
  9.5 Extension of the General Model to Multiple Mobility Classes
    9.5.1 Compartmental Description
    9.5.2 Diffusion Approximation
    9.5.3 Deterministic Approximation
    9.5.4 Simulation Study
  9.6 Data Preparation
    9.6.1 Triple Normalisation
    9.6.2 Double Normalisation
    9.6.3 Single Normalisation
  9.7 Application
    9.7.1 Data
    9.7.2 Bayesian Estimation
    9.7.3 Least Squares Estimation
    9.7.4 Conclusion
  9.8 Diffusion-Coupled FRAP
  9.9 Conclusion and Outlook
  References

Conclusion and Appendix
10 Summary and Future Work
  10.1 Summary
  10.2 Future Work
A Benchmark Models
  A.1 Geometric Brownian Motion
  A.2 Omstein-Uhlenbeck Process
  A.3 Cox-Ingersoll-Ross Process
  References
B Miscellaneous
  B.1 Difference Operators
  B.2 Lipschitz Continuity for SIR Models
    B.2.1 Standard SIR Model
    B.2.2 Multitype SIR Model
  B.3 On the Choice of the Update Interval
  B.4 Posteriori Densities for the Omstein-Uhlenbeck Process
  B.5 Inefficiency Factors
  B.6 Path Proposals in the Latent Data Framework
  B.7 Derivation of Radon-Nikodym Derivatives
  B.8 Derivation of Acceptance Probability
  References
C Supplementary Material for Application II
  C.1 Diffusion Approximations
    C.1.1 One Mobility Class
    C.1.2 Multiple Mobility Classes
  C.2 Calculation of Deterministic Process
  C.3 Estimation Results
  C.4 Diffusion-Coupled Model
Index

内容摘要
《扩散过程推断理论及其在生命科学中的应用》是一部理论和实践相结合的参考图书，是数学、统计学以及一些前沿学科生物信息学和生物学的不可或缺的学习参考用书。和已有的同类型图书相比，本书是在一个框架下处理扩散用的模型和推断，因此囊括了实际应用中所需的所有步骤，有基本的微分方程、概率论和统计知识即可学习该书。

主编推荐
《扩散过程推断理论及其在生命科学中的应用》是一部理论和实践相结合的参考图书，作者为数学、统计学以及一些前沿学科生物信息学和生物学的不可或缺的学习参考用书。

— 没有更多了 —