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Książki na temat „Random observations”

Autor: Grafiati

Data publikacji: 5 października 2024

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1

University, Emory, red. I have never tasted any bad ice cream and other random observations. Decatur, G.A: Looking Glass Books, 1997.

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Weinberg, Israel Louis. Settings at random: Observations, experiences, ideas : Brick Lane, London E1, 1915-1987. [Wembley] (]28, Blenheim Gardens, East Lane, Wembley, HA9 7NP]): [I.L. Weinberg], 1987.

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Hyde, Henry J. Catch the burning flag: Speeches and random observations of Henry Hyde ; [foreword by Robert Novak]. New York: National Review Books, 2008.

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Näther, Wolfgang. Effective observation of random fields. Leipzig: Teubner, 1985.

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Davies, Merton E. Rand's role in the evolution of balloon and satellite observation systems and related U.S. space technology. Santa Monica, CA: Rand Corp., 1988.

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Short, Nevil. Random Observations. Unknown Publisher, 2021.

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7

Richard, Smith. Random Observations. Certainty Perspectives, 2020.

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8

Maready, Kenan. Random Thoughts and Observations. Independently Published, 2019.

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9

McWhorter, Jenn. Random Rants and Other Observations. Lulu Press, Inc., 2008.

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Single Exposures (Random Observations on Photography, Art & Creativity). LensWork Publishing, 2005.

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Edward S. (Edward Selden) 18 Spaulding. Random Observations of an Idle Mind, While Contemplating an Open Fire. Creative Media Partners, LLC, 2018.

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Sanford, Robin. STATUS QUOTE-Recapping a Decade of Random Observations Via Social Media. Lulu Press, Inc., 2020.

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Edward S. (Edward Selden) Spaulding. Random Observations of an Idle Mind, While Contemplating an Open Fire. Creative Media Partners, LLC, 2022.

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Edward S. (Edward Selden) 18 Spaulding. Random Observations Of An Idle Mind, While Contemplating An Open Fire. Franklin Classics, 2018.

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Sanford, Robin. STATUS QUOTE-Recapping a Decade of Random Observations Via Social Media. Lulu Press, Inc., 2020.

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Edward S (Edward Selden) 18 Spaulding. Random Observations of an Idle Mind, While Contemplating an Open Fire. Franklin Classics Trade Press, 2018.

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Edward S. (Edward Selden) Spaulding. Random Observations of an Idle Mind, While Contemplating an Open Fire. Creative Media Partners, LLC, 2022.

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18

Random Commuter Observations: Living the Dream on the Way to Work. Post Hill Press, 2018.

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Barstow, Lauren. Forty-Five Minute Sit-Down Comedy: With Observations, Opinions, and Random Ramblings. Page Publishing Inc., 2018.

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Estimation of Stochastic Processes With Missing Observations. New York, USA: Nova Science Publishers Inc., 2019.

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21

Clausen, Fred. Divine Order of Our Random Life: A Collection of Teachable Moments and Human Observations. Clausen, Charles Fred, 2022.

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McGee, J. J. Rome's Randumbness: A compilation of my 9,012 daily observations, random thoughts, and life advice... CreateSpace Independent Publishing Platform, 2017.

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Nicholas, Paul. Baby Brother's Window: Random Observations of the Good, the Bad, and the Hard to Explain. Independently Published, 2014.

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Catch the burning flag: Speeches and random observations of Henry Hyde ; [foreword by Robert Novak]. New York: National Review Books, 2008.

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Bro, Jay. Thought Bubbles, a Collection of Cartoons: Graphic Explorations of Random Thoughts, Misconstrued Concepts, and Offbeat Observations. jbro, 2022.

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Bro, Jay. Thought Bubbles, a Collection of Cartoons: Graphic Explorations of Random Thoughts, Misconstrued Concepts, and Offbeat Observations. jbro, 2022.

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27

This May Not Have Been the Best Idea : Digging in the Middle East: Musings, Ramblings, Subjective Observations, and Even a Few Random Facts. iUniverse, Incorporated, 2016.

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Lau, J. This May Not Have Been the Best Idea : Digging in the Middle East: Musings, Ramblings, Subjective Observations, and Even a Few Random Facts. iUniverse, 2016.

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29

Cheng, Russell. Change-Point Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0011.

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This chapter investigates change-point (hazard rate) probability models for the random survival time in some population of interest. A parametric probability distribution is assumed with parameters to be estimated from a sample of observed survival times. If a change-point parameter, denoted by τ‎, is included to represent the time at which there is a discrete change in hazard rate, then the model is non-standard. The profile log-likelihood, with τ‎ as profiling parameter, has a discontinuous jump at every τ‎ equal to a sampled value, becoming unbounded as τ‎ tends to the largest observation. It is known that maximum likelihood estimation can still be used provided the range of τ‎ is restricted. It is shown that the alternative maximum product of spacings method is consistent without restriction on τ‎. Censored observations which commonly occur in survival-time data can be accounted for using Kaplan-Meier estimation. A real data numerical example is given.

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30

Bouchaud, Jean-Philippe. Random matrix theory and (big) data analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797319.003.0006.

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This chapter reviews methods from random matrix theory to extract information about a large signal matrix C (for example, a correlation matrix arising in big data problems), from its noisy observation matrix M. The chapter shows that the replica method can be used to obtain both the spectral density and the overlaps between noise-corrupted eigenvectors and the true ones, for both additive and multiplicative noise. This allows one to construct optimal rotationally invariant estimators of C based on the observation of M alone. This chapter also discusses the case of rectangular correlation matrices and the problem of random singular value decomposition.

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McCleary, Richard, David McDowall i Bradley J. Bartos. ARIMA Algebra. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0002.

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The goal of Chapter 2 is to derive the properties of common processes and, based on these properties, to develop a general scheme for classifying processes. Stationary processes includes white noise, moving average (MA), and autoregressive (AR) processes. MA and AR models can approximate mixed ARMA models. A lag or backshift operator is used to solve ARIMA models for time series observations or random shocks. Covariance functions are derived for each of the common processes.Maximum likelihood estimates are introduced for the purposes of estimating autoregressive and moving average parameters.

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32

Fyodorov, Yan, i Dmitry Savin. Condensed matter physics. Redaktorzy Gernot Akemann, Jinho Baik i Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.35.

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This article discusses some applications of concepts from random matrix theory (RMT) to condensed matter physics, with emphasis on phenomena, predicted or explained by RMT, that have actually been observed in experiments on quantum wires and quantum dots. These observations range from universal conductance fluctuations (UCF) to weak localization, non-Gaussian thermopower distributions, and sub-Poissonian shot noise. The article first considers the UCF phenomenon, nonlogarithmic eigenvalue repulsion, and sub-Poissonian shot noise in quantum wires before analysing level and wave function statistics, scattering matrix ensembles, conductance distribution, and thermopower distribution in quantum dots. It also examines the effects (not yet observed) of superconductors on the statistics of the Hamiltonian and scattering matrix.

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33

Aye, Goodness C. Wealth inequality and CO2 emissions in emerging economies: The case of BRICS. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/918-1.

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As the world battles with the triple problems of social, economic, and environmental challenges, it has become important to focus both policy and research efforts on these. Therefore, this study examines the effect of wealth inequality on CO2 emissions in five emerging economies: Brazil, Russia, India, China, and South Africa. The top decile of wealth share was used as a measure of wealth inequality, while CO2 emissions per capita were used as a measure of CO2 emissions. GDP per capita, population, and financial development (domestic credit to the private sector) were included as control variables. A balanced panel dataset of annual observations from 2000 to 2014 for these countries was used. Both fixed and random effects panel models were estimated, but the Hausman test favoured the use of the fixed effects model. The results based on the fixed effects panel regression model show that wealth inequality, GDP per capita, and population have positive effects on CO2 emissions, while financial development has a negative effect.

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Bayley, Robert. Variationist Sociolinguistics. Redaktorzy Robert Bayley, Richard Cameron i Ceil Lucas. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199744084.013.0001.

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The central ideas of variationist sociolinguistics are that an understanding of language requires an understanding of variable as well as categorical processes, and that the variation witnessed at all levels of language is not random. Rather, linguistic variation is characterized by orderly or “structured heterogeneity.” In addition, synchronic variation is often a reflection of diachronic change. This chapter reviews representative studies and outlines the main assumptions underlying the variationist approach. It presents an example of variationist analysis, using the well-known case of variation between Spanish null and overt subject personal pronouns. Then, the chapter considers a number of relatively recent developments in variationist sociolinguistics including the expansion of the variationist paradigm into new areas such as second-language acquisition and sign linguistics, as well as recent work that combines ethnographic observation and quantitative analysis.

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Scarani, Valerio. Bell Nonlocality. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198788416.001.0001.

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Nonlocality was discovered by John Bell in 1964, in the context of the debates about quantum theory, but is a phenomenon that can be studied in its own right. Its observation proves that measurements are not revealing pre-determined values, falsifying the idea of “local hidden variables” suggested by Einstein and others. One is then forced to make some radical choice: either nature is intrinsically statistical and individual events are unspeakable, or our familiar space-time cannot be the setting for the whole of physics. As phenomena, nonlocality and its consequences will have to be predicted by any future theory, and may possibly play the role of foundational principles in these developments. But nonlocality has found a role in applied physics too: it can be used for “device-independent” certification of the correct functioning of random number generators and other devices. After a self-contained introduction to the topic, this monograph on nonlocality presents the main tools and results following a logical, rather than a chronological, order.

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Chico, Tita. The Experimental Imagination. Stanford University Press, 2018. http://dx.doi.org/10.11126/stanford/9781503605442.001.0001.

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This book is about experimental imagination in the British Enlightenment. It tells the story of how literariness came to be distinguished from its epistemological sibling, science, as a source of truth about the natural and social worlds. Early scientists used metaphor to define the phenomena they studied. They likewise used metaphor to imagine themselves into their roles as experimentalists. Late seventeenth- and early eighteenth-century British literature includes countless references to early science to make the case for the epistemological superiority of literary knowledge, whose truths challenge the dominant account of the scientific revolution as the sine qua non epistemological innovation of the long eighteenth century. The Experimental Imagination considers traditional scientific writings alongside poems, plays, and prose works by canonical and non-canonical authors to argue that ideas about science facilitated new forms of evidence and authority. The noisy satiric rancor and quiet concern that science generated among science advocates, dramatists, essayists, and poets reveal a doubled epistemological trajectory: experimental observation utilizes imaginative speculation and imaginative fancy enables new forms of understanding. Early scientific practice requires yet often obscures that imaginative impulse, which literary knowledge embraces as a way of understanding the world at large. Reciprocally, the period’s theory of aesthetics arises from the observational protocols of science, ultimately laying claim to literature as epistemologically superior. Early science finds its intellectual and conceptual footing in the metaphoric thinking available through literary knowledge, and literary writers wield science as a trope for the importance and unique insights of literary knowledge.

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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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