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Books on the topic 'Bayes Algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Algorithmic algebraic combinatorics and Gröbner bases. Heidelberg: Springer, 2009.

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2

Klin, Mikhail, Gareth A. Jones, Aleksandar Jurišić, Mikhail Muzychuk, and Ilia Ponomarenko, eds. Algorithmic Algebraic Combinatorics and Gröbner Bases. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01960-9.

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3

Li, Huishi. Noncommutative Gr bner Bases and Filtered-Graded Transfer. Berlin: Springer-Verlag Berlin/Heidelberg, 2002.

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4

Diophantine equations and power integral bases: New computational methods. Boston: Birkhäuser, 2002.

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5

Varlamov, Oleg. 18 examples of mivar expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1248446.

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Many years of research on mivar technologies of logical artificial intelligence have allowed us to create a new powerful, versatile and fast tool, which is called "multidimensional open gnoseological active net" — "multidimensional open gnoseological active net: MOGAN". This tool allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format, and it can be used to model cause-and-effect relationships in different subject areas and create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with "Big Knowledge". The reader, after studying this tutorial, you will be able to create mivar expert system with the help of CASMI Wi!Mi. Designed for students, bachelors, masters and postgraduate students studying artificial intelligence methods, as well as for users, experts and specialists, creating a system of information processing and management, mivar models, expert systems, automated control systems, systems of decision support and Recommender systems.

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6

Kravchenko, Igor', Maksim Glinskiy, Sergey Karcev, Viktor Korneev, and Diana Abdumuminova. Resource-saving plasma technology in the repair of processing equipment. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1083289.

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In the monograph methodological bases of selection of method of coating, design of technological processes of hardening and recovery of the wearing surfaces of parts using a systems engineering analysis and information support technologist. The mathematical model of plasma spraying of materials with different thermal conductivity and methods criteria for evaluation of technical and technological opportunities of a plasma coating method. Describes the methods and results of experimental studies, the analysis of the conditions and causes of loss of efficiency of processing equipment APK. The proposed scientific and methodical approach to the justification of expediency of the recovery and strengthening of the working bodies and parts expensive imported technological equipment. The proposed mathematical model describing the physical processes in plasma coating for various applications. The structure of the algorithm for solving the task of hardening and recovery of worn parts plasma methods on the basis of the integrated CAE system. This monograph is intended for employees of scientific research institutions, specialists of machine-building production and enterprises of technical service, as well as teachers, postgraduates and students of agricultural engineering areas of training.

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7

Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

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8

McGlothin, Charles C. Ambient sound in the ocean induced by heavy precipitation and the subsequent predictability of rainfall rate. Monterey, California: Naval Postgraduate School, 1991.

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9

International Phoenix Conference on Computers and Communications (13th 1994 Phoenix, Ariz.). 1994 IEEE 13th Annual International Phoenix Conference on Computers and Communications: April 12-15, 1994, Phoenix, Arizona. Piscataway, N.J: IEEE, 1994.

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10

Efficient structures for geometric data management. Berlin: Springer-Verlag, 1988.

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11

Jones, Gareth A., Mikhail Klin, Ilia Ponomarenko, Aleksandar Jurisic, and Mikhail Muzychuk. Algorithmic Algebraic Combinatorics and Gröbner Bases. Springer, 2014.

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12

Algorithmic Algebraic Combinatorics and Gröbner Bases. Springer, 2009.

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13

Virginia, Torczon, and Langley Research Center, eds. Rank ordering and positive bases in pattern search algorithms. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1996.

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14

Gaál, István. Diophantine Equations and Power Integral Bases: Theory and Algorithms. Birkhäuser, 2019.

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15

Oscar, Cordón, ed. Genetic fuzzy systems: Evolutionary tuning and learning of fuzzy knowledge bases. Singapore: World Scientific, 2001.

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16

STR, a simple and efficient algorithm for R-Tree packing. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1997.

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17

Miclet, Laurent, Yves Kodratoff, Antoine Cornuéjols, and Tom Mitchell. Apprentissage artificiel : Concepts et algorithmes. Eyrolles, 2002.

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18

Saito, Mutsumi, Nobuki Takayama, and Bernd Sturmfels. Groebner Deformations of Hypergeometric Differential Equations, Algorithms and Computation in Mathematics, Volume 6. Springer, 2000.

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19

Computational Intelligence: Eine methodische Einführung in Künstliche Neuronale Netze, Evolutionäre Algorithmen, Fuzzy-Systeme und Bayes-Netze (German Edition). Springer Vieweg, 2015.

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20

Borgelt, Christian, Frank Klawonn, Rudolf Kruse, Christian Moewes, Matthias Steinbrecher, and Georg Ruß. Computational Intelligence: Eine methodische Einführung in Künstliche Neuronale Netze, Evolutionäre Algorithmen, Fuzzy-Systeme und Bayes-Netze (German Edition). Vieweg+Teubner Verlag, 2011.

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21

Yu, Angela J. Bayesian Models of Attention. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.025.

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Traditionally, attentional selection has been thought of as arising naturally from resource limitations, with a focus on what might be the most apt metaphor, e.g. whether it is a ‘bottleneck’ or ‘spotlight’. However, these simple metaphors cannot account for the specificity, flexibility, and heterogeneity of the way attentional selection manifests itself in different behavioural contexts. A recent body of theoretical work has taken a different approach, focusing on the computational needs of selective processing, relative to environmental constraints and behavioural goals. They typically adopt a normative computational framework, incorporating Bayes-optimal algorithms for information processing and action selection. This chapter reviews some of this recent modelling work, specifically in the context of attention for learning, covert spatial attention, and overt spatial attention.

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22

The symbolic computation and automated analysis of trajectories: Semi-annual status report for NASA Grant NAG2-513. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1991.

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23

Devraj, Venkat S. Oracle 24x7 Tips and Techniques. Osborne/McGraw-Hill, 1999.

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24

Devraj, Venkat S. Oracle 24x7 Tips and Techniques. Osborne/McGraw-Hill, 1999.

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