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Книги з теми "Probabilistic-statistical method"

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Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Reliability: Probabilistic models and statistical methods. Englewood Cliffs, N.J: Prentice Hall, 1995.

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2

Neuenschwander, Daniel. Probabilistic and Statistical Methods in Cryptology. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b97045.

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3

Mari, Jean-François. Probabilistic and statistical methods in computer science. Boston: Kluwer Academic Publishers, 2001.

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4

Mari, Jean-François. Probabilistic and Statistical Methods in Computer Science. Boston, MA: Springer US, 2001.

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5

Mari, Jean-François, and René Schott. Probabilistic and Statistical Methods in Computer Science. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-6280-8.

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6

Eerola, Mervi. Probabilistic causality in longitudinal studies. New York: Springer-Verlag, 1994.

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7

Sharov, Valery Dmitryevich, Vadim Vadimovich Vorobyov, and Dmitry Alexandrovich Zatuchny. Probabilistic-Statistical Methods for Risk Assessment in Civil Aviation. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0092-0.

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8

Stapor, Katarzyna. Introduction to Probabilistic and Statistical Methods with Examples in R. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45799-0.

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9

Neuenschwander, Daniel. Probabilistic and statistical methods in cryptology: An introduction by selected topics. Berlin: Springer, 2004.

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10

Brown, Andrew M. Probabilistic component mode synthesis of nondeterministic substructures. Washington, DC: [National Aeronautics and Space Administration, 1997.

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11

Brown, Andrew M. Development of a probabilistic component mode synthesis method for the analysis of non-deterministic substructures. Washington, DC: [National Aeronautics and Space Administration, 1995.

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12

Leemis, Lawrence. Reliability: Probabilistic Models and Statistical Methods. Prentice Hall, 1994.

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13

Leemis, Lawrence. Reliability: Probabilistic Models and Statistical Methods. Prentice Hall, 1994.

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14

C, Ionescu D., and Limnios N, eds. Statistical and probabilistic models in reliability. Boston: Birkhäuser, 1999.

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15

Schott, René, and Jean-François Mari. Probabilistic and Statistical Methods in Computer Science. Springer, 2000.

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16

Boland, Philip J. Statistical and Probabilistic Methods in Actuarial Science. Chapman and Hall/CRC, 2007. http://dx.doi.org/10.1201/9781584886969.

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17

Probabilistic Causality in Longitudinal Studies. Springer, 2011.

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18

Ghosh, Sucharita, Rafal Kulik, Yuanhua Feng, and Jan Beran. Long-Memory Processes: Probabilistic Properties and Statistical Methods. Springer, 2013.

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19

Ghosh, Sucharita, Rafal Kulik, Yuanhua Feng, and Jan Beran. Long-Memory Processes: Probabilistic Properties and Statistical Methods. Springer, 2016.

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20

Resnick, Sidney I. I. Heavy-Tail Phenomena: Probabilistic and Statistical Modeling. Springer, 2010.

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21

Boland, Philip J. Statistical and Probabilistic Methods in Actuarial Science (Interdisciplinary Statistics). Chapman & Hall/CRC, 2007.

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22

E, Barndorff-Nielsen O., Jensen J. L, and Kendall W. S, eds. Networks and chaos: Statistical and probabilistic aspects. London: Chapman & Hall, 1993.

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23

Ola Kristensson, Per. Statistical Language Processing for Text Entry. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0003.

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Анотація:

In this chapter we explain how methods from statistical language processing serve as a foundation for the design of probabilistic text entry methods and error correction methods. We review concepts from information theory and language modelling and explain how to design a statistical decoder for text entry—a generative probabilistic model based on the token-passing paradigm. We then present five example applications of statistical language processing for text entry: correcting typing mistakes, enabling fast typing on a smartwatch, improving prediction in augmentative and alternative communication, enabling dwell-free eye-typing and intelligently supporting error correction of probabilistic text entry. We then discuss the limitations of the models presented in this chapter and highlight the importance of establishing solution principles based on engineering science and empirical research in order to guide the design of probabilistic text entry.

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24

Probabilistic mesomechanical fatigue model. [Washington, DC: National Aeronautics and Space Administration, 1997.

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25

Stapor, Katarzyna. Introduction to Probabilistic and Statistical Methods with Examples in R. Springer, 2020.

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26

Statistical and Probabilistic Models in Reliability (Statistics for Industry and Technology). Birkhäuser Boston, 1998.

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27

George C. Marshall Space Flight Center., ed. Development of a probabilistic dynamic synthesis method for the analysis of nondeterministic structures. [Marshall Space Flight Center, Ala.]: National Aeronautics and Space Administration, Marshall Space Flight Center, 1998.

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28

George C. Marshall Space Flight Center., ed. Development of a probabilistic dynamic synthesis method for the analysis of nondeterministic structures. [Marshall Space Flight Center, Ala.]: National Aeronautics and Space Administration, Marshall Space Flight Center, 1998.

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29

Wavelet Methods for Time Series Analysis (Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge University Press, 2006.

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30

Wavelet Methods for Time Series Analysis (Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge University Press, 2000.

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31

Samuelsson, Christer. Statistical Methods. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0019.

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Анотація:

Statistical methods now belong to mainstream natural language processing. They have been successfully applied to virtually all tasks within language processing and neighbouring fields, including part-of-speech tagging, syntactic parsing, semantic interpretation, lexical acquisition, machine translation, information retrieval, and information extraction and language learning. This article reviews mathematical statistics and applies it to language modelling problems, leading up to the hidden Markov model and maximum entropy model. The real strength of maximum-entropy modelling lies in combining evidence from several rules, each one of which alone might not be conclusive, but which taken together dramatically affect the probability. Maximum-entropy modelling allows combining heterogeneous information sources to produce a uniform probabilistic model where each piece of information is formulated as a feature. The key ideas of mathematical statistics are simple and intuitive, but tend to be buried in a sea of mathematical technicalities. Finally, the article provides mathematical detail related to the topic of discussion.

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32

Marwala, Tshilidzi, Sondipon Adhikari, and Ilyes Boulkaibet. Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering. Wiley & Sons, Incorporated, John, 2016.

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33

Kendall, Wilfrid S., and J. L. Jensen. Networks and Chaos - Statistical and Probabilistic Aspects (Monographs on Statistics and Applied Probability). Chapman & Hall/CRC, 1993.

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34

F, Fossum Arlo, American Society of Mechanical Engineers. Applied Mechanics Division., and ASME Summer Applied Mechanics Conference (1992 : Tempe, Ariz.), eds. Probabilistic method in geomechanics: Presented at the ASME Summer Mechanics and Materials conferences, Tempe, Arizona, April 28-May 1, 1992. New York, N.Y: American Society of Mechanical Engineers, 1992.

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35

Thomas, Leonard, and John S. J. Hsu. Bayesian Methods: An Analysis for Statisticians and Interdisciplinary Researchers (Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge University Press, 2001.

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36

Braun, John, and John Maindonald. Data Analysis and Graphics Using R: An Example-based Approach (Cambridge Series in Statistical and Probabilistic Mathematics). 2nd ed. Cambridge University Press, 2006.

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37

Veech, Joseph A. Habitat Ecology and Analysis. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198829287.001.0001.

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Анотація:

Habitat is crucial to the survival and reproduction of individual organisms as well as persistence of populations. As such, species-habitat relationships have long been studied, particularly in the field of wildlife ecology and to a lesser extent in the more encompassing discipline of ecology. The habitat requirements of a species largely determine its spatial distribution and abundance in nature. One way to recognize and appreciate the over-riding importance of habitat is to consider that a young organism must find and settle into the appropriate type of habitat as one of the first challenges of life. This process can be cast in a probabilistic framework and used to better understand the mechanisms behind habitat preferences and selection. There are at least six distinctly different statistical approaches to conducting a habitat analysis – that is, identifying and quantifying the environmental variables that a species most strongly associates with. These are (1) comparison among group means (e.g., ANOVA), (2) multiple linear regression, (3) multiple logistic regression, (4) classification and regression trees, (5) multivariate techniques (Principal Components Analysis and Discriminant Function Analysis), and (6) occupancy modelling. Each of these is lucidly explained and demonstrated by application to a hypothetical dataset. The strengths and weaknesses of each method are discussed. Given the ongoing biodiversity crisis largely caused by habitat destruction, there is a crucial and general need to better characterize and understand the habitat requirements of many different species, particularly those that are threatened and endangered.

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38

Ghosh, Arunabh. Making It Count. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691179476.001.0001.

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In 1949, at the end of a long period of wars, one of the biggest challenges facing leaders of the new People's Republic of China was how much they did not know. The government of one of the world's largest nations was committed to fundamentally reengineering its society and economy via socialist planning while having almost no reliable statistical data about their own country. This book is the history of efforts to resolve this “crisis in counting.” The book explores the choices made by political leaders, statisticians, academics, statistical workers, and even literary figures in attempts to know the nation through numbers. It shows that early reliance on Soviet-inspired methods of exhaustive enumeration became increasingly untenable in China by the mid-1950s. Unprecedented and unexpected exchanges with Indian statisticians followed, as the Chinese sought to learn about the then-exciting new technology of random sampling. These developments were overtaken by the tumult of the Great Leap Forward (1958–1961), when probabilistic and exhaustive methods were rejected and statistics was refashioned into an ethnographic enterprise. By acknowledging Soviet and Indian influences, the book not only revises existing models of Cold War science but also globalizes wider developments in the history of statistics and data. Anchored in debates about statistics and its relationship to state building, the book offers fresh perspectives on China's transition to socialism.

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