广义概率论发展前景——关于趣味数学与置信函数实际应用的一些原创观点(英文)
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- ISBN:9787576706772
- 装帧:平装-胶订
- 册数:暂无
- 重量:暂无
- 开本:其他
- 页数:231
- 出版时间:2023-03-01
- 条形码:9787576706772 ; 978-7-5767-0677-2
内容简介
本书是一本英文数学专著。在本书中,将展示计算机视觉和信任积分之间的卓有成效的交互作用是如何切实地激发这两个领域的重大进步的.从方法论的角度出发,本书在第二部分中阐述并讨论了作为数学对象的置信函数的几何和代数性质的新奇的理论结果,重点是对不确定性的透视的“几何方法”和证据冲突的代数解的阐释.在第三部分中,我们展示了这些理论是如何从重要的计算机视觉问题中产生并发展起来的(比如对象跟踪、数据关联和物体定位),而证据形式主义又可以为这些问题提供有趣的新的解决方案.*后,还初步研究了将全概率的概念推广到置信函数的相关内容,从赋予证据理论一套完整的评估和推断工具的角度来看,这个研究对所有科学家和实践者都是有好处的.
目录
Preface
CHAPTER 1. Introduction
Part 1. Belief calculus
CHAPTER 2. Shafer's mathematical theory of evidence
1. Belief functions
2. Dempster's rule of combination
3. Simple and separable support functions
4. Families of compatible frames of discernment
5. Support functions
6. Impact of the evidence
7. Quasi support functions
8. Consonant belief functions
CHAPTER 3. State of the art
1. The alternative interpretations of belief functions
2. Frameworks and approaches
3. Conditional belief functions
4. Statistical inference and estimation
5. Decision making
6. Efficient implementation of belief calculus
7. Continuous belief functions
8. Other theoretical developments
9. Relation with other mathematical theories of uncertainty
10. Applications
Part 2. Advances
CHAPTER 4. A geometric approach to belief calculus
1. The space of belief functions
2. Simplicial form of the belief space
3. The bundle structure of the belief space
4. Global geometry of Dempster's rule
5. Pointwise geometry of Dempster's rule
6. Applications of the geometric approach
7. Conclusive comments
CHAPTER 5. Algebraic structure of the families of compatible frames
1. Axiom analysis
2. Monoidal structure of families of frames
3. Lattice structure of families of frames
4. Semimodular structure of families of frames
CHAPTER 6. Algebra of independence and conflict
I. Independence of frames and Dempster's combination
2. An algebraic study of independence of flames
3. Independence on lattices versus independence of frames
4. Perspectives
5. Conclusive comments
Part 3. Visions
CHAPTER 7. Data association and the total belief theorem
1. The data association problem
2. The total belief theorem
3. The restricted total belief theorem
4. Conclusive comments
CHAPTER 8. Belief Modeling Regression
1. Scenario
2. Learning evidential models
3. Regression
4. Assessing evidential models
5. Results on human pose estimation
6. Discussion
7. Towards evidential tracking
8. Conclusive comments
Part 4. Conclusions
CHAPTER 9. Conclusions
Bibliography
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