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廣義概率論發展前景——關于趣味數學與置信函數實際應用的一些原創觀點(英文) 版權信息
- ISBN:9787576706772
- 條形碼:9787576706772 ; 978-7-5767-0677-2
- 裝幀:平裝-膠訂
- 冊數:暫無
- 重量:暫無
- 所屬分類:>>
廣義概率論發展前景——關于趣味數學與置信函數實際應用的一些原創觀點(英文) 內容簡介
本書是一本英文數學專著。在本書中,將展示計算機視覺和信任積分之間的卓有成效的交互作用是如何切實地激發這兩個領域的重大進步的.從方法論的角度出發,本書在第二部分中闡述并討論了作為數學對象的置信函數的幾何和代數性質的新奇的理論結果,重點是對不確定性的透視的“幾何方法”和證據沖突的代數解的闡釋.在第三部分中,我們展示了這些理論是如何從重要的計算機視覺問題中產生并發展起來的(比如對象跟蹤、數據關聯和物體定位),而證據形式主義又可以為這些問題提供有趣的新的解決方案.*后,還初步研究了將全概率的概念推廣到置信函數的相關內容,從賦予證據理論一套完整的評估和推斷工具的角度來看,這個研究對所有科學家和實踐者都是有好處的.
廣義概率論發展前景——關于趣味數學與置信函數實際應用的一些原創觀點(英文) 目錄
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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