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Libros sobre el tema "Computational inference method"

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Publicado: 25 de mayo de 2024

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1

Istrail, Sorin, Michael Waterman y Andrew Clark, eds. Computational Methods for SNPs and Haplotype Inference. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b96286.

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2

Learning and inference in computational systems biology. Cambridge, Mass: MIT Press, 2010.

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3

Neil, Lawrence, ed. Learning and inference in computational systems biology. Cambridge, MA: MIT Press, 2010.

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4

Heard, Nick. An Introduction to Bayesian Inference, Methods and Computation. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82808-0.

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5

Lo, Andrew W. Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. Cambridge, MA: National Bureau of Economic Research, 2000.

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6

Workshop for Dialogue on Reverse Engineering Assessment and Methods (2006 New York, N.Y.). Reverse engineering biological networks: Opportunities and challenges in computational methods for pathway inference. Boston, Mass: Published by Blackwell Publishing on behalf of the New York Academy of Sciences, 2007.

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7

Sorin, Istrail, Waterman Michael S y Clark Andrew G. 1954-, eds. Computational methods for SNPs and Haplotype inference: DIMACS/RECOMB satellite workshop, Piscataway, NJ, USA, November 21-22, 2002 : revised papers. Berlin: Springer-Verlag, 2004.

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8

Diagrams 2010 (2010 Portland, Or.). Diagrammatic representation and inference: 6th international conference, Diagrams 2010, Portland, OR, USA, August 9-11, 2010 : proceedings. Berlin: Springer, 2010.

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9

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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10

Desmarais, Bruce A. y Skyler J. Cranmer. Statistical Inference in Political Networks Research. Editado por Jennifer Nicoll Victor, Alexander H. Montgomery y Mark Lubell. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.8.

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Researchers interested in statistically modeling network data have a well-established and quickly growing set of approaches from which to choose. Several of these methods have been regularly applied in research on political networks, while others have yet to permeate the field. This chapter reviews the most prominent methods of inferential network analysis for both cross-sectionally and longitudinally observed networks, including (temporal) exponential random graph models, latent space models, the quadratic assignment procedure, and stochastic actor oriented models. For each method, the chapter summarizes its analytic form, identifies prominent published applications in political science, and discusses computational considerations. It concludes with a set of guidelines for selecting a method for a given application.
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11

(Editor), Sorin Istrail, Michael Waterman (Editor) y Andrew Clark (Editor), eds. Computational Methods for SNPs and Haplotype Inference. Springer, 2004.

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12

Heard, Nick. Introduction to Bayesian Inference, Methods and Computation. Springer International Publishing AG, 2021.

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13

Heard, Nick. Introduction to Bayesian Inference, Methods and Computation. Springer International Publishing AG, 2022.

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14

Tang, Xiaoying, Thomas Fletcher y Michael I. Miller, eds. Bayesian Estimation and Inference in Computational Anatomy and Neuroimaging: Methods & Applications. Frontiers Media SA, 2019. http://dx.doi.org/10.3389/978-2-88945-984-1.

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15

Zhang, Hao. Multivariate Geostatistical Models: Inference and Computation (Monographs on Statistics and Applied Probability). Chapman & Hall/CRC, 2009.

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16

Wikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.

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The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the tools to perform inference and prediction in the presence of uncertainty. In particular, the field of spatial statistics considers inference and prediction for uncertain processes that exhibit dependence in space and/or time. Traditionally, this is done descriptively through the characterization of the first two moments of the process, one expressing the mean structure and one accounting for dependence through covariability.Historically, there are three primary areas of methodological development in spatial statistics: geostatistics, which considers processes that vary continuously over space; areal or lattice processes, which considers processes that are defined on a countable discrete domain (e.g., political units); and, spatial point patterns (or point processes), which consider the locations of events in space to be a random process. All of these methods have been used in the climate sciences, but the most prominent has been the geostatistical methodology. This methodology was simultaneously discovered in geology and in meteorology and provides a way to do optimal prediction (interpolation) in space and can facilitate parameter inference for spatial data. These methods rely strongly on Gaussian process theory, which is increasingly of interest in machine learning. These methods are common in the spatial statistics literature, but much development is still being done in the area to accommodate more complex processes and “big data” applications. Newer approaches are based on restricting models to neighbor-based representations or reformulating the random spatial process in terms of a basis expansion. There are many computational and flexibility advantages to these approaches, depending on the specific implementation. Complexity is also increasingly being accommodated through the use of the hierarchical modeling paradigm, which provides a probabilistically consistent way to decompose the data, process, and parameters corresponding to the spatial or spatio-temporal process.Perhaps the biggest challenge in modern applications of spatial and spatio-temporal statistics is to develop methods that are flexible yet can account for the complex dependencies between and across processes, account for uncertainty in all aspects of the problem, and still be computationally tractable. These are daunting challenges, yet it is a very active area of research, and new solutions are constantly being developed. New methods are also being rapidly developed in the machine learning community, and these methods are increasingly more applicable to dependent processes. The interaction and cross-fertilization between the machine learning and spatial statistics community is growing, which will likely lead to a new generation of spatial statistical methods that are applicable to climate science.
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17

Waterman, Michael, Clark Andrew y Sorin Istrail. Computational Methods for SNPs and Haplotype Inference: DIMACS/RECOMB Satellite Workshop, Piscataway, NJ, USA, November 21-22, 2002, Revised Papers. Springer London, Limited, 2004.

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18

Green, Peter, Kanti Mardia, Vysaul Nyirongo y Yann Ruffieux. Bayesian modelling for matching and alignment of biomolecules. Editado por Anthony O'Hagan y Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.2.

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This article describes Bayesian modelling for matching and alignment of biomolecules. One particular task where statistical modelling and inference can be useful in scientific understanding of protein structure is that of matching and alignment of two or more proteins. In this regard, statistical shape analysis potentially has something to offer in solving biomolecule matching and alignment problems. The article discusses the use of Bayesian methods for shape analysis to assist with understanding the three-dimensional structure of protein molecules, with a focus on the problem of matching instances of the same structure in the CoMFA (Comparative Molecular Field Analysis) database of steroid molecules. It introduces a Bayesian hierarchical model for pairwise matching and for alignment of multiple configurations before concluding with an overview of some advantages of the Bayesian approach to problems in protein bioinformatics, along with modelling and computation issues, alternative approaches, and directions for future research.
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19

Dwyer, Tim, Helen Purchase y Aidan Delaney. Diagrammatic Representation and Inference: 8th International Conference, Diagrams 2014, Melbourne, VIC, Australia, July 28 - August 1, 2014, Proceedings. Springer, 2014.

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20

Diagrammatic Representation and Inference: 8th International Conference, Diagrams 2014, Melbourne, VIC, Australia, July 28 - August 1, 2014, Proceedings. Springer, 2014.

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21

Dornschneider, Stephanie. Hot Contention, Cool Abstention. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190693916.001.0001.

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Why did people mobilize for the Arab Spring? While existing research has focused on the roles of authoritarian regimes, oppositional structures, and social grievances in the movement, these explanations fail to address differences in the behavior of individuals, overlooking the fact that even when millions mobilized for the Arab Spring, the majority of the population stayed at home. To investigate this puzzle, this book traces the reasoning processes by which individuals decided to join the uprisings or to refrain from doing so. Drawing from original ethnographic interviews with protestors and non-protestors in Egypt and Morocco, Dornschneider utilizes qualitative methods and computational modeling to identify the main components of reasoning processes: beliefs, inferences (directed connections between beliefs), and decisions. Bridging the psychology literature on reasoning and the political science literature on protest, this book systematically traces how decisions about participating in the Arab Spring were made. It shows that decisions to join the uprisings were “hot,” meaning they were based on positive emotions, while decisions to stay at home were “cool,” meaning they were based on safety considerations. Hot Contention, Cool Abstention adds to the extensive literature on political uprisings, offering insights on how and why movements start, stall, and evolve.
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