Mathematical Theory of Arguments for Statistical Evidence

• 1. The Theory of Generalized Functional Models.- 2. The Plausibility and Likelihood Functions.- 3. Hints on Continuous Frames and Gaussian Linear Systems.- 4. Assumption-Based Reasoning with Classical Regression Models.- 5. Assumption-Based Reasoning with General Gaussian Linear Systems.- 6. Gaussian Hints as a Valuation System.- 7. Local Propagation of Gaussian Hints.- 8. Application to the Kaiman Filter.- References.

From the reviews: "The book, clearly written, is structured in two parts comprising two and six chapters, respectively. ... The overall quality of the book is very good, the material is well organized and notations and terminology are unified. It is a very good presentation of the state of research in the area of modeling reasoning and an insightful reference for the statistician." (Evdokia Xekalaki, Zentralblatt MATH, Vol. 1100 (2), 2007)