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Ïîèñê êíèã, ñîäåðæàùèõ: Prediction
| Êíèãà | Ñòðàíèöû äëÿ ïîèñêà | | Sornette D. — Critical phenomena in natural sciences | | | Khosrowpour M. — Encyclopedia Of Information Science And Technology | 910 | | Doob J.L. — Stochastic processes | see Chapter XII | | Oksendal B. — Stochastic differential equations : an introduction with applications | 103 | | Good P.I., Hardin J.W. — Common Errors in Statistics : (and How to Avoid Them) | 140, 159—161, 173 | | Lindsey J.K. — Applying generalized linear models | 33, 63, 70, 71, 77—80, 89, 91, 96, 100, 118, 119, 169, 174, 186 | | Miranker W.L. — Numerical Methods for Stiff Equations and Singular Perturbation Problems | 38—40 | | Rawlings J.O., Pantula S.G., Dickey D.A. — Applied Regression Analysis: A Research Tool | 6, 90, 175, 176, 206, 207, 249 | | Winograd T. — Understanding computers and cognition | 15—16, 34, 95 | | Ehlers J.F. — Mesa and Trading Market Cycles: Forecasting and Trading Strategies from the Creator of MESA | 90, 91, 110, 111, 114, 115, 118, 119, 121, 122, 126 | | Wilensky R. — Planning and Understanding | 45—46 | | Skorokhod A.V., Prokhorov Y.V. (Ed) — Basic Principles and Applications of Probability Theory | 17, 257ff | | Meisel W.S. — Computer-oriented approach to pattern recognition | 5 | | Bigus J.P. — Data mining with neural networks | 39—40 | | Casella G. — Statistical Design | 101, 140 | | Kyburg H.E., Teng Ch.M. — Uncertain Inference | 281, 284, 286 | | Kohonen T. — Self-organizing maps | 194, 208, 368 | | Cappe O., Ryden T., Moulines E. — Inference in Hidden Markov Models | 54 | | Graybill F.A., Iyer H.K. — Regression Analysis | 1, 73, 80, 99, 219 | | Resnick S.I. — A probability path | 164 | | Nickerson R. — Cognition and Chance The Psychology of Probabilistic Reasoning | 292—303 | | Chatfield C. — The Analysis of Time Series: An Introduction | 6, 90 | | Ben-Zvi D. — The Challenge of Developing Statistical Literacy, Reasoning and Thinking | 12, 22, 32, 85, 97, 102, 104, 111, 112, 128, 147, 160, 163, 167, 202—204, 210, 228, 278, 303, 309, 310—314, 330, 349, 398, 400, 403 | | Gershenfeld N. — The Nature of Mathematical Modelling-Neil Gershenfeld | 186 | | Oksendal B. — Stochastic Differential Equations: An Introduction With Applications | 104 | | Lawless J.F. — Statistical Models and Methods for Lifetime Data | 194, 201 | | Galwey N.V. — Introduction to Mixed Modelling: Beyond Regression and Analysis of Variance | 87, 118, 158, 160—162, 164, 173,193, 206—209, 276, 291, 292 | | Stone C.J.D. — Course in Probability and Statistics | 316—322 | | Scott A. — Neuroscience: a mathematical primer | 30, 32 | | Patterson D.A., Hennessy J.L. — Computer Organization and Design: The Hardware/Software Interface | 382, 421—423 | | Campbell N.R. — What is science? | 59, 69—71, 87—89, 91 | | Ablameyko S., Goras L., Gori M. — Neural networks for instrumentation, measurement and related industrial applications | 120, 122, 129, 135, 138, 142 | | Katayama T., Sugimoto S. — Statistical Methods in Control and Signal Processing | 41, 54, 56—57 | | Tanimoto S.L. — The elements of artificial intelligence. An introduction using LISP | 475 | | Gerstner W., Kistler W.M. — Spiking Neuron Models | 429 | | Hilborn R.C. — Chaos and nonlinear dynamics | 37—39, 516—517 | | Furui S. — Digital Speech Processing, Synthesis, and Recognition | 145 | | Bernardo J.M., Smith A.F.M. — Bayesian Theory | 7, 10, 407, 408, 445, 454 | | Shanbhag D.N. (ed.), Rao C.R. (ed.) — Stochastic Processes - Modelling and Simulation | 874, 883, 885, 888, 890, 894, 919, 924 | | Hsiao C. — Analysis of panel data | 158 | | Serra J. — Image Analysis and Mathematical Morphology | 576—579 | | Hannan E. J. — Multiple time series | 127—163 | | Attwood T.K., Parry-Smith D.J. — Introduction to bioinformatics | 7—8, 14, 40, 82, 93, 95, 163—164 | | Knuth D.E. — The art of computer programming (vol. 3 Sorting and Searching) | see “Forecasting” | | Arnold B.C., Balakrishnan N., Nagaraja H.N. — A First Course in Order statistics | 180—183 | | Aslrom K.J. — Introduction to Stochastic Control Theory | 210 | | Bracewell R.N. — The Fourier Transform and its applications | 250 | | Haykin S. — Kalman filtering and neural networks | 3, 124 | | Hoffman B. — Strange Story of the Quantum | 15, 151— 152, 175—177, 181 | | Kotz S., Johnson N.L. — Breakthroughs in Statistics: Volume 1: Foundations and Basic Theory | 153, 463 | | Emanuel Parzen — Stochastic processes (Classics in Applied Mathematics) | 3, 43, 113, 114 | | Auletta G. — Foundations and Interpretation of Quantum Mechanics | 443—445, 453 | | Efron B. — Large-Scale Inference. Empirical Bayes Methods for Estimation, Testing, and Prediction | xi, 1, 7—10, 28, 211—217, 223, 228, 229, 234, 235, 237—241 | | Zhang Y. — Visual Information Representation, Communication and Image Processing | 181 | | Nelson W.B. — Recurrent events data analysis for product repairs, disease recurrences, and other applications | 31, 54, 125 | | Efron B. — Large-Scale Interference. Empirical Bayes Methods for Estimation, Testing, and Prediction | xi, 1, 7—10, 28, 211—217, 223, 228, 229, 234, 235, 237—241 | | Mullin T. — The nature of chaos | 153—154 | | Rosenblatt M. — Random processes | 97, 160ff., 181 | | Balakrishnan N., Rao C.R. — Handbook of Statistics (Vol. 17): Order Statistics: Applications | 431—435, 448 | | Sutton O.G. — Mathematics in action | 16, 194 | | Bracewell R. — The Fourier Transform and Its Applications | 250 | | Marks R.J.II. — The Joy of Fourier | 471, 472, 505 | | Kneale W. — Probability and Induction | 234n., 251 | | Gelman A., Carlin J.B., Stern H.S. — Bayesian data analysis | see "Posterior predictive distribution" | | Ashby W.R. — An introduction to cybernetics | 132 | | Elden L. — Numerical Linear Algebra and Applications in Data Mining | 77 | | Hristev R.M. — The artificial neural network book | 191 | | Mario Bunge — Foundations of Physics | 80—83, 142, 276 | | Bridgman P.W. — The Nature of Thermodynamics | 101, 163 | | Jeffreys H. — Theory of probability | 1, 4, 13, 14, 40 | | Bickel P., Doksum K. — Mathematical statistics | 16, 32 | | Planck M. — Scientific Autobiography And Other Papers | 121, 145 | | Kotz S., Johnson N.L. — Breakthroughs in Statistics: Volume 2: Methodology and Distribution | 377 | | Rabin S. — AI Game Programming Wisdom 4 (AI Game Programming Wisdom (W/CD)) | see also "Unpredictability" | | Kendall M.G. — The advanced theory of statistics (vol. 2) | see "Forecasting" | | Rice J.A. — Mathematical statistics and data analysis | 140 | | Vanmarcke Erik — Random Fields : Analysis and Synthesis | 18, 349—351, 361, see also "Optimal linear estimation" | | Bunge M. — Foundations of Physics | 80—83, 142, 276 | | Kline M. — Mathematics for the Nonmathematician | 548 | | Berger J.O., Wolpert R.L. — The Likelihood Principle | 36—39, 41.1, 180—181, 194 | | Carnap R. — Foundations of logic and mathematics | 1, 15 | | Blin-Stoyle R.J. — Eureka! Physics of particles, matter and the universe | 3 | | von der Malsburg C., von Seelen W. — Artificial Neural Networks - ICANN 96: 6th International Conference, Bochum, Germany, July 16 - 19, 1996, Proceedings | 263, 785 | | Alt F.L., Rubinoff M. — Advances in computers.Volume 3 | see "Court decisions" | | Voit E. — Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists | 64, 65, 98, 117, 122, 126, 128, 140, 152, 226, 230, 234, 236, 243, 251, 262, 290, 295, 296, 297, 307, 323, 324, 325, 326, 341, 353, 356, 377, 384, 387, 390, 392, 399, 401, 402 | | Serra J. — Image Analysis and Mathematical Morphology | 576—579 |
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