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Books on the topic 'Probability constraints'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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1

Piunovskiy, A. B. Optimal Control of Random Sequences in Problems with Constraints. Dordrecht: Springer Netherlands, 1997.

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2

Ryan, M. J. Economic choice constrained games and the nature of probability. Hull: University of Hull, Department of Economics, 1992.

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3

Tree-based graph partitioning constraint. London: ISTE, 2011.

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4

Xiaoqi, Yang, ed. Lagrange-type functions in constrained non-convex optimization. Boston: Kluwer Academic Publishers, 2003.

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5

Constrained principal component analysis and related techniques. Boca Raton: CRC, Taylor & Francis Group, 2014.

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6

Kotzen, Matthew. Probability in Epistemology. Edited by Alan Hájek and Christopher Hitchcock. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199607617.013.32.

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In recent years, probabilistic approaches to epistemological questions have become increasingly influential. This chapter surveys a number of the most significant ways in which probability is relevant to contemporary epistemology. Topics surveyed include: the debate surrounding the connection between full and partial beliefs; synchronic rational constraints on credences including probabilism, regularity, reflection, and the principal principle; diachronic rational constraints on credences including conditionalization and de se updating; the application of the requirement of total evidence; evidential probability, focusing on the theories of Henry Kyburg and Timothy Williamson; sharp and fuzzy credences; likelihood arguments, including the fine-tuning argument for Design; dogmatism and its critics; and transmission failure, focusing on the work of Crispin Wright.

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7

McGrew, Timothy. The Spirit of Cromwell’s Rule. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198746904.003.0015.

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One of the central complaints about Bayesian probability is that it places no constraints on individual subjectivity in one’s initial probability assignments. Those sympathetic to Bayesian methods have responded by adding restrictions motivated by broader epistemic concerns about the possibility of changing one’s mind. This chapter explores some cases where, intuitively, a straightforward Bayesian model yields unreasonable results. Problems arise in these cases not because there is something wrong with the Bayesian formalism per se but because standard textbook illustrations teach us to represent our inferences in simplified ways that break down in extreme cases. It also explores some interesting limitations on the extent to which successive items of evidence ought to induce us to change our minds when certain screening conditions obtain.

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8

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Random graph ensembles. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0003.

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This chapter presents some theoretical tools for defining random graph ensembles systematically via soft or hard topological constraints including working through some properties of the Erdös-Rényi random graph ensemble, which is the simplest non-trivial random graph ensemble where links appear between two nodes with a fixed probability p. The chapter sets out the central representation of graph generation as the result of a discrete-time Markovian stochastic process. This unites the two flavours of graph generation approaches – because they can be viewed as simply moving forwards or backwards through this representation. It is possible to define a random graph by an algorithm, and then calculate the associated stationary probability. The alternative approach is to specify sampling weights and then to construct an algorithm that will have these weights as the stationary probabilities upon convergence.

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9

Lorca, Xavier. Tree-Based Graph Partitioning Constraint. Wiley & Sons, Incorporated, John, 2013.

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10

Lorca, Xavier. Tree-Based Graph Partitioning Constraint. Wiley & Sons, Incorporated, John, 2013.

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11

Lorca, Xavier. Tree-Based Graph Partitioning Constraint. Wiley & Sons, Incorporated, John, 2013.

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12

Lorca, Xavier. Tree-Based Graph Partitioning Constraint. Wiley & Sons, Incorporated, John, 2013.

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13

Lorca, Xavier. Tree-Based Graph Partitioning Constraint. Wiley & Sons, Incorporated, John, 2011.

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14

Yang, Xiao-qi, and Alexander M. Rubinov. Lagrange-type Functions in Constrained Non-Convex Optimization. Springer, 2013.

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15

Hitchcock, Christopher. Probabilistic Causation. Edited by Alan Hájek and Christopher Hitchcock. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199607617.013.41.

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This chapter will explore a variety of projects that aim to characterize causal concepts using probability. These are, somewhat arbitrarily, divided into four categories. First, a tradition within philosophy that has aimed to define, or at least constrain, causation in terms of conditional probability is discussed. Secondly, the use of causal Bayes nets to represent causal relations, to facilitate inferences from probabilities to causal relations, and to ‘identify’ causal quantities in probabilistic terms is discussed. Thirdly, efforts to measure causal strength in probabilistic terms are reviewed, with particular attention to the significance of these measures in the context of epidemiology. Finally, attempts are discussed to analyze the relation of ‘actual causation’ (sometimes called ‘singular causation’) using probability.

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16

Takane, Yoshio. Constrained Principal Component Analysis and Related Techniques. Taylor & Francis Group, 2016.

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17

Takane, Yoshio. Constrained Principal Component Analysis and Related Techniques. Taylor & Francis Group, 2013.

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18

Takane, Yoshio. Constrained Principal Component Analysis and Related Techniques. Taylor & Francis Group, 2020.

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19

Takane, Yoshio. Constrained Principal Component Analysis and Related Techniques. Taylor & Francis Group, 2016.

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20

Snijders, Tom A. B., and Mark Pickup. Stochastic Actor Oriented Models for Network Dynamics. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.10.

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Stochastic Actor Oriented Models for Network Dynamics are used for the statistical analysis of longitudinal network data collected as a panel. The probability model defines an unobserved stochastic process of tie changes, where social actors add new ties or drop existing ties in response to the current network structure; the panel observations are snapshots of the resulting changing network. The statistical analysis is based on computer simulations of this process, which provides a great deal of flexibility in representing data constraints and dependence structures. In this Chapter we begin by defining the basic model. We then explicate a new model for nondirected ties, including several options for the specification of how pairs of actors coordinate tie changes. Next, we describe coevolution models. These can be used to model the dynamics of several interdependent sets of variables, such as the analysis of panel data on a network and the behavior of the actors in the network, or panel data on two or more networks. We finish by discussing the differences between Stochastic Actor Oriented Models and some other longitudinal network models. A major distinguishing feature is the treatment of time, which allows straightforward application of the model to panel data with different time lags between waves. We provide a variety of applications in political science throughout.

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21

Rubinov, A., and Xiao-qi Yang. Lagrange-type Functions in Constrained Non-Convex Optimization (Applied Optimization). Springer, 2003.

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22

Davidson, Ian, Kiri Wagstaff, and Sugato Basu. Constrained Clustering: Advances in Algorithms, Theory, and Applications. Taylor & Francis Group, 2008.

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23

Kumar, Vipin, Ian Davidson, Kiri Wagstaff, and Sugato Basu. Constrained Clustering: Advances in Algorithms, Theory, and Applications. Taylor & Francis Group, 2008.

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24

(Editor), Sugato Basu, Ian Davidson (Editor), and Kiri Wagstaff (Editor), eds. Constrained Clustering: Advances in Algorithms, Theory, and Applications (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series). Chapman & Hall/CRC, 2008.

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25

Jappelli, Tullio, and Luigi Pistaferri. Lifetime Uncertainty. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199383146.003.0011.

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Lifetime uncertainty represents an additional risk that affects intertemporal choice, because consumers may live longer than expected and run the risk of exhausting the resources accumulated for retirement. Lifetime uncertainty introduces an incentive to consume earlier in life because consumers discount future utility at a higher rate. Second, since in each period there is some positive probability that the consumer will not survive to the next period, the terminal condition on wealth corresponds effectively to a liquidity constraint. Third, with lifetime uncertainty, the decumulation of wealth by the elderly is slower than predicted by the life-cycle model. Finally, the model with lifetime uncertainty generates transfers of wealth across generations even without an express bequest motive, through what we can term involuntary or accidental bequests. The chapter highlights the necessity of accounting for lifetime uncertainty when interpreting empirical age-wealth profiles estimated from microeconomic data.

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26

Meder, Björn, and Ralf Mayrhofer. Diagnostic Reasoning. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.23.

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This chapter discusses diagnostic reasoning from the perspective of causal inference. The computational framework that provides the foundation for the analyses—probabilistic inference over graphical causal structures—can be used to implement different models that share the assumption that diagnostic inferences are guided and constrained by causal considerations. This approach has provided many critical insights, with respect to both normative and empirical issues. For instance, taking into account uncertainty about causal structures can entail diagnostic judgments that do not reflect the empirical conditional probability of cause given effect in the data, the classic, purely statistical norm. The chapter first discusses elemental diagnostic inference from a single effect to a single cause, then examines more complex diagnostic inferences involving multiple causes and effects, and concludes with information acquisition in diagnostic reasoning, discussing different ways of quantifying the diagnostic value of information and how people decide which information is diagnostically relevant.

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27

Milkman, Ruth. Union Responses to Workforce Feminization in the United States. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252040320.003.0007.

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This chapter examines how labor unions responded to workforce feminization that began in the 1970s. It first places the relationship of women to unions in historical perspective before analyzing empirical data on inter-union variations in the extent of women's representation in union membership and leadership in the late twentieth century, as well as variations in the extent and nature of attention to “women's issues” on the part of unions. It then explores the dynamics of union organizing in the 1980s, showing that workplaces with large female majorities were the most readily organized in that period—as measured by the probability of winning National Labor Relations Board (NLRB) union representation elections. It also considers the growing commitment of some unions in the 1970s and 1980s to gender equality issues and to incorporating women into positions of leadership. Finally, it discusses the innovative gender politics that has emerged in unions least constrained by the forces of deunionization or patriarchal traditions.

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28

Md. Ali, Azham. The political economy of external auditing in Malaysia 1957 - 1997. UUM Press, 1999. http://dx.doi.org/10.32890/9839559656.

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This work investigates the role and contribution of external auditing as practiced in Malaysian society in the first 40 years since independence in 1957.It applies the political economic approach, which emphasises the social relations aspects of professional activity rather than economic forces alone.The political economic approach is applied by utilising an enlarged exogenous framework of processual change analysis.This particular interpretive framework views external auditing in Malaysia over the forty year period (1957-1997) as an open, dynamic social system comprising two pattern transformations.The study focuses specifically on the historical development of, and environment influences on the countrys audit practice and provides insights into the operational form of contemporary audit practice and the historical, social, economic and political determinants of that form.The writer extrapolates that in all probability, the future of auditing in Malaysia will continue to be constrained by unresolved problems; audit in Malaysia seems to be case of the triumph of hope over experience.

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