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100   $a20060629d2006      uy             0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aData analysis$b[electronic resource] $ea Bayesian tutorial /$fD.S. Sivia with J. Skilling
205   $a2nd ed.
210   $aOxford $cOxford University Press$d2006
215   $a1 online resource (259 p.)
225 1 $aOxford science publications
300   $aPrevious ed.: 1996.
311   $a0-19-856831-2 
311   $a0-19-856832-0 
320   $aIncludes bibliographical references and index.
327   $aContents; PART I: THE ESSENTIALS; 1. The basics; 2. Parameter estimation I; 3. Parameter estimation II; 4. Model selection; 5. Assigning probabilities; PART II: ADVANCED TOPICS; 6. Non-parametric estimation; 7. Experimental design; 8. Least-squares extensions; 9. Nested sampling; 10. Quantification; A. Gaussian integrals; B. Cox's derivation of probability; Bibliography; Index; A; B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; Q; R; S; T; U; V; W; X
330   $aOne of the strengths of this book is the author's ability to motivate the use of Bayesian methods through simple yet effective examples. - Katie St. Clair MAA Reviews
410  0$aOxford science publications.
606   $aMaximum entropy method
606   $aMaximum principles (Mathematics)
606   $aEngineering mathematics
606   $aScience$xMathematics
615  0$aMaximum entropy method.
615  0$aMaximum principles (Mathematics)
615  0$aEngineering mathematics.
615  0$aScience$xMathematics.
676   $a519.5
700   $aSivia$b D. S$0535097
701   $aSkilling$b J$g(John)$01708374
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910823241703321
996   $aData analysis$94097317
997   $aUNINA