000798144 000__ 04777cam\a2200541Mu\4500 000798144 001__ 798144 000798144 005__ 20230306143508.0 000798144 006__ m\\\\\o\\d\\\\\\\\ 000798144 007__ cr\cn\nnnunnun 000798144 008__ 170819s2017\\\\sz\\\\\\o\\\\\000\0\eng\d 000798144 019__ $$a1001548111$$a1004438027$$a1004729538 000798144 020__ $$a9783319646718$$q(electronic book) 000798144 020__ $$a3319646710$$q(electronic book) 000798144 020__ $$z9783319646701 000798144 020__ $$z3319646702 000798144 035__ $$aSP(OCoLC)on1001368259 000798144 035__ $$aSP(OCoLC)1001368259$$z(OCoLC)1001548111$$z(OCoLC)1004438027$$z(OCoLC)1004729538 000798144 040__ $$aEBLCP$$beng$$cEBLCP$$dN$T$$dYDX$$dGW5XE$$dOCLCF$$dUAB 000798144 049__ $$aISEA 000798144 050_4 $$aQA276.8 000798144 050_4 $$aQA1-939 000798144 08204 $$a519.5/44$$223 000798144 08204 $$a510 000798144 1001_ $$aNagy, Ivan. 000798144 24510 $$aAlgorithms and programs of dynamic mixture estimation :$$bunified approach to different types of components. 000798144 260__ $$aCham :$$bSpringer,$$c2017. 000798144 300__ $$a1 online resource (116 pages) 000798144 336__ $$atext$$btxt$$2rdacontent 000798144 337__ $$acomputer$$bc$$2rdamedia 000798144 338__ $$aonline resource$$bcr$$2rdacarrier 000798144 4901_ $$aSpringerBriefs in Statistics 000798144 504__ $$aIncludes bibliographical references. 000798144 5050_ $$aAcknowledgements; Contents; 1 Introduction; 1.1 On Dynamic Mixtures; 1.2 General Conventions; 2 Basic Models; 2.1 Regression Model; 2.1.1 Estimation; 2.1.2 Point Estimates; 2.1.3 Prediction; 2.2 Categorical Model; 2.2.1 Estimation; 2.2.2 Point Estimates; 2.2.3 Prediction; 2.3 State-Space Model; 2.3.1 State Estimation; 3 Statistical Analysis of Dynamic Mixtures; 3.1 Dynamic Mixture; 3.2 Unified Approach to Mixture Estimation; 3.2.1 The Component Part; 3.2.2 The Pointer Part; 3.2.3 Main Subtasks of Mixture Estimation; 3.2.4 General Algorithm; 3.3 Mixture Prediction; 3.3.1 Pointer Prediction 000798144 5058_ $$a3.3.2 Data Prediction4 Dynamic Mixture Estimation; 4.1 Normal Regression Components; 4.1.1 Algorithm; 4.1.2 Simple Program; 4.1.3 Comments; 4.2 Categorical Components; 4.2.1 Algorithm; 4.2.2 Simple Program; 4.2.3 Comments; 4.3 State-Space Components; 4.3.1 Algorithm; 4.3.2 Simple Program; 4.3.3 Comments; 5 Program Codes; 5.1 Main Program; 5.1.1 Comments; 5.2 Subroutines; 5.2.1 Initialization of Estimation; 5.2.2 Computation of Proximities; 5.2.3 Update of Component Statistics; 5.3 Collection of Programs; 6 Experiments; 6.1 Mixture with Regression Components; 6.1.1 Well-Separated Components 000798144 5058_ $$a6.1.2 Weak Components6.1.3 Reduced Number of Components; 6.1.4 High-Dimensional Output; 6.1.5 Big Noise; 6.2 Mixture with Categorical Components; 6.3 Mixture with State-Space Components; 6.4 Case Studies; 6.4.1 Static Normal Components; 6.4.2 Dynamic Normal Components; 7 Appendix A (Supporting Notions); 7.1 Useful Matrix Formulas ; 7.2 Matrix Trace ; 7.3 Dirac and Kronecker Functions ; 7.4 Gamma and Beta Functions ; 7.5 The Bayes Rule ; 7.6 The Chain Rule; 7.7 The Natural Conditions of Control ; 7.8 Conjugate Dirichlet Distribution; 7.8.1 The Normalization Constant of Dirichlet Distribution 000798144 5058_ $$a7.8.2 Statistics Update with the Conjugate Dirichlet Distribution7.8.3 The Parameter Point Estimate of the Categorical Model; 7.8.4 Data Prediction with Dirichlet Distribution; 7.9 Conjugate Gauss-Inverse-Wishart Distribution ; 7.9.1 Statistics Update for the Normal Regression Model; 7.9.2 The Parameter Point Estimate of the Regression Model ; 7.9.3 The Proximity Evaluation; 8 Appendix B (Supporting Programs); 8.1 Simulation Programs; 8.1.1 The Simulation of Pointer Values; 8.1.2 The Simulation of Mixture with Regression Components; 8.1.3 The Simulation of Mixture with Discrete Components 000798144 5058_ $$a8.1.4 The Simulation of Mixture with State-Space Components8.2 Supporting Subroutines; 8.2.1 Scilab Start Settings; 8.2.2 The Point Estimation of a Normal Regression Model; 8.2.3 The Value of a Normal Multivariate Distribution; 8.2.4 Discrete Regression Vector Coding; 8.2.5 Kalman Filter; 8.2.6 Matrix Upper -- Lower Factorization; 8.2.7 Transition Table Normalization; 8.2.8 The Approximation of Normal Pdfs by a Single Pdf; Appendix References 000798144 506__ $$aAccess limited to authorized users. 000798144 588__ $$aDescription based on print version record. 000798144 650_0 $$aEstimation theory. 000798144 650_0 $$aRegression analysis$$xMathematical models. 000798144 7001_ $$aSuzdaleva, Evgenia. 000798144 77608 $$iPrint version:$$aNagy, Ivan$$tAlgorithms and Programs of Dynamic Mixture Estimation : Unified Approach to Different Types of Components$$dCham : Springer International Publishing,c2017$$z9783319646701 000798144 830_0 $$aSpringerBriefs in statistics. 000798144 852__ $$bebk 000798144 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-64671-8$$zOnline Access$$91397441.1 000798144 909CO $$ooai:library.usi.edu:798144$$pGLOBAL_SET 000798144 980__ $$aEBOOK 000798144 980__ $$aBIB 000798144 982__ $$aEbook 000798144 983__ $$aOnline 000798144 994__ $$a92$$bISE