02072oam 2200481 450 991070406030332120151020132339.0(CKB)5470000002437051(OCoLC)885282053(EXLCZ)99547000000243705120140805d1982 ua 0engurbn|||||||||txtrdacontentcrdamediacrrdacarrierThermodynamic properties of selected minerals in the system Al₂O₃-CaO-SiO₂-H₂O at 298.15 K and 1 bar (10⁵ Pascals) pressure and at higher temperatures /by B.S. Hemingway, J.L. Haas, Jr., and G.R. Robinson, Jr[Reston, Va.] :United States Department of the Interior, Geological Survey,1982.Washington :United States Government Printing Office.1 online resource (iii, 70 pages)Geological Survey Bulletin ;1544Title from title screen (viewed Aug. 1, 2014).This report is supplemental to U.S. Geological Survey Bulletin 1452.U.S. G.P.O. sales statement incorrect in publication.Includes bibliographical references.Thermodynamic properties of selected minerals in the system Al₂O₃-CaO-SiO₂-H₂O at 298.15 K and 1 bar Aluminum silicatesThermal propertiesAluminum silicatesThermal propertiesfastAluminum silicatesThermal properties.Aluminum silicatesThermal properties.Hemingway Bruce S.1394189Haas John L.Jr.,1932-2012,Robinson Gilpin R.Jr.,Geological Survey (U.S.),COPCOPOCLCOOCLCFGPOBOOK9910704060303321Thermodynamic properties of selected minerals in the system Al₂O₃-CaO-SiO₂-H₂O at 298.15 K and 1 bar (10⁵ Pascals) pressure and at higher temperatures3506998UNINA03954nam 2200469 450 991079949390332120240119114117.0981-9938-41-4(CKB)29449601300041(MiAaPQ)EBC31051649(Au-PeEL)EBL31051649(MiAaPQ)EBC31031964(Au-PeEL)EBL31031964(EXLCZ)992944960130004120240119d2023 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierWAIC and WBIC with Python Stan 100 Exercises for Building Logic /Joe SuzukiFirst edition.Singapore :Springer Nature Singapore Pte Ltd,[2023]©20231 online resource (249 pages)9789819938407 Includes bibliographical references and index.Intro -- Preface: Sumio Watanabe-Spreading the Wonder of Bayesian Theory -- One-Point Advice for Those Who Struggle with Math -- Features of This Series -- Contents -- 1 Overview of Watanabe's Bayes -- 1.1 Frequentist Statistics -- 1.2 Bayesian Statistics -- 1.3 Asymptotic Normality of the Posterior Distribution -- 1.4 Model Selection -- 1.5 Why are WAIC and WBIC Bayesian Statistics? -- 1.6 What is ``Regularity'' -- 1.7 Why is Algebraic Geometry Necessary for Understanding WAIC and WBIC? -- 1.8 Hironaka's Desingularization, Nothing to Fear -- 1.9 What is the Meaning of Algebraic Geometry's λ in Bayesian Statistics? -- 2 Introduction to Watanabe Bayesian Theory -- 2.1 Prior Distribution, Posterior Distribution, and Predictive Distribution -- 2.2 True Distribution and Statistical Model -- 2.3 Toward a Generalization Without Assuming Regularity -- 2.4 Exponential Family -- 3 MCMC and Stan -- 3.1 MCMC and Metropolis-Hastings Method -- 3.2 Hamiltonian Monte Carlo Method -- 3.3 Stan in Practice -- 3.3.1 Binomial Distribution -- 3.3.2 Normal Distribution -- 3.3.3 Simple Linear Regression -- 3.3.4 Multiple Regression -- 3.3.5 Mixture of Normal Distributions -- 4 Mathematical Preparation -- 4.1 Elementary Mathematics -- 4.1.1 Matrices and Eigenvalues -- 4.1.2 Open Sets, Closed Sets, and Compact Sets -- 4.1.3 Mean Value Theorem and Taylor Expansion -- 4.2 Analytic Functions -- 4.3 Law of Large Numbers and Central Limit Theorem -- 4.3.1 Random Variables -- 4.3.2 Order Notation -- 4.3.3 Law of Large Numbers -- 4.3.4 Central Limit Theorem -- 4.4 Fisher Information Matrix -- 5 Regular Statistical Models -- 5.1 Empirical Process -- 5.2 Asymptotic Normality of the Posterior Distribution -- 5.3 Generalization Loss and Empirical Loss -- 6 Information Criteria -- 6.1 Model Selection Based on Information Criteria -- 6.2 AIC and TIC -- 6.3 WAIC.6.4 Free Energy, BIC, and WBIC -- 7 Algebraic Geometry -- 7.1 Algebraic Sets and Analytical Sets -- 7.2 Manifold -- 7.3 Singular Points and Their Resolution -- 7.4 Hironaka's Theorem -- 7.5 Local Coordinates in Watanabe Bayesian Theory -- 8 The Essence of WAIC -- 8.1 Formula of State Density -- 8.2 Generalization of the Posterior Distribution -- 8.3 Properties of WAIC -- 8.4 Equivalence with Cross-Validation-Like Methods -- 9 WBIC and Its Application to Machine Learning -- 9.1 Properties of WBIC -- 9.2 Calculation of the Learning Coefficient -- 9.3 Application to Deep Learning -- 9.4 Application to Gaussian Mixture Models -- 9.5 Non-informative Prior Distribution -- References -- -- Index.Bayesian statistical decision theoryLogic, Symbolic and mathematicalBayesian statistical decision theory.Logic, Symbolic and mathematical.519.542Suzuki Joe846228MiAaPQMiAaPQMiAaPQBOOK9910799493903321WAIC and WBIC with Python Stan3872434UNINA