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Age-Period-Cohort Analysis

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ISBN: 9781466507524 Year: DOI: 10.1201/b13902 Language: English
Publisher: Taylor & Francis
Subject: Mathematics
Added to DOAB on : 2020-09-01 00:03:32
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Age-Period-Cohort Analysis: New Models, Methods, and Empirical Applications is based on a decade of the authors’ collaborative work in age-period-cohort (APC) analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs: age-by-time period tables of population rates or proportions, repeated cross-section sample surveys, and accelerated longitudinal panel studies. The authors show how the empirical application of the models to various problems leads to many fascinating findings on how outcome variables develop along the age, period, and cohort dimensions. The book makes two essential contributions to quantitative studies of time-related change. Through the introduction of the GLMM framework, it shows how innovative estimation methods and new model specifications can be used to tackle the "model identification problem" that has hampered the development and empirical application of APC analysis. The book also addresses the major criticism against APC analysis by explaining the use of new models within the GLMM framework to uncover mechanisms underlying age patterns and temporal trends. Encompassing both methodological expositions and empirical studies, this book explores the ways in which statistical models, methods, and research designs can be used to open new possibilities for APC analysis. It compares new and existing models and methods and provides useful guidelines on how to conduct APC analysis. For empirical illustrations, the text incorporates examples from a variety of disciplines, such as sociology, demography, and epidemiology. Along with details on empirical analyses, software and programs to estimate the models are available on the book’s web page.

Reichenbach’s Paradise. Constructing the Realm of Probabilstic Common “Causes”

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ISBN: 9783110372717 Year: Pages: 113 DOI: 10.2478/9783110372717 Language: English
Publisher: De Gruyter
Subject: Philosophy --- Statistics
Added to DOAB on : 2014-11-17 10:26:35
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Since its introduction by Hans Reichenbach, many philosophers have claimed to refute the idea – known as the common cause principle – that any surprising correlation between any two factors that do not directly influence one another is due to some common cause. For example, falsity of the principle is frequently inferred from falsifiability of Bell’s inequalities. The author demonstrates, however, that the situation is not so straightforward. There is more than one version of the principle formulated with the use of different variants of Reichenbach-inspired notions; their falsity still remains an open question. The book traces different formulations of the principle and provides proofs of a few pertinent theorems, settling the relevant questions in various probability spaces. In exploring mathematical and philosophical issues surrounding the principle, the book offers both philosophical insight and mathematical rigor.

Possibly imperfect ontologies for effective information retrieval

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ISBN: 9783866441903 Year: Pages: XIV, 272 p. DOI: 10.5445/KSP/1000007206 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Computer Science
Added to DOAB on : 2019-07-30 20:01:58
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Ontologies and semantic metadata can theoretically solve all problems of traditional full-text search engines. In practice, however, they are always imperfect. This work analyzed whether the negative effect of ontology imperfection is higher than the positive effect of exploiting the ontology features for IR. To answer this question, a complete ontology-based information retrieval system was implemented and thoroughly evaluated.

Quantum Probability and Randomness

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ISBN: 9783038977148 9783038977155 Year: Pages: 276 DOI: 10.3390/books978-3-03897-715-5 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Physics (General)
Added to DOAB on : 2019-04-25 16:37:17
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The last few years have been characterized by a tremendous development of quantum information and probability and their applications, including quantum computing, quantum cryptography, and quantum random generators. In spite of the successful development of quantum technology, its foundational basis is still not concrete and contains a few sandy and shaky slices. Quantum random generators are one of the most promising outputs of the recent quantum information revolution. Therefore, it is very important to reconsider the foundational basis of this project, starting with the notion of irreducible quantum randomness. Quantum probabilities present a powerful tool to model uncertainty. Interpretations of quantum probability and foundational meaning of its basic tools, starting with the Born rule, are among the topics which will be covered by this issue. Recently, quantum probability has started to play an important role in a few areas of research outside quantum physics&mdash;in particular, quantum probabilistic treatment of problems of theory of decision making under uncertainty. Such studies are also among the topics of this issue.

Keywords

quantum logic --- groups --- partially defined algebras --- quasigroups --- viable cultures --- quantum information theory --- bit commitment --- protocol --- entropy --- entanglement --- orthogonality --- quantum computation --- Gram–Schmidt process --- quantum probability --- potentiality --- complementarity --- uncertainty relations --- Copenhagen interpretation --- indefiniteness --- indeterminism --- causation --- randomness --- quantum information --- quantum dynamics --- entanglement --- algebra --- causality --- geometry --- probability --- quantum information theory --- realism --- reality --- entropy --- correlations --- qubits --- probability representation --- Bayes’ formula --- quantum entanglement --- three-qubit random states --- entanglement classes --- entanglement polytope --- anisotropic invariants --- quantum random number --- vacuum state --- maximization of quantum conditional min-entropy --- quantum logics --- quantum probability --- holistic semantics --- epistemic operations --- Bell inequalities --- algorithmic complexity --- Borel normality --- Bayesian inference --- model selection --- random numbers --- quantum-like models --- operational approach --- information interpretation of quantum theory --- social laser --- social energy --- quantum information field --- social atom --- Bose–Einstein statistics --- bandwagon effect --- social thermodynamics --- resonator of social laser --- master equation for socio-information excitations --- quantum contextuality --- Kochen–Specker sets --- MMP hypergraphs --- Greechie diagrams --- quantum foundations --- probability --- irreducible randomness --- random number generators --- quantum technology --- entanglement --- quantum-like models for social stochasticity --- contextuality

Applied and Computational Statistics

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ISBN: 9783039281763 9783039281770 Year: Pages: 104 DOI: 10.3390/books978-3-03928-177-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Mathematics
Added to DOAB on : 2020-01-30 16:39:46
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Research without statistics is like water in the sand; the latter is necessary to reap the benefits of the former. This collection of articles is designed to bring together different approaches to applied statistics. The studies presented in this book are a tiny piece of what applied statistics means and how statistical methods find their usefulness in different fields of research from theoretical frames to practical applications such as genetics, computational chemistry, and experimental design. This book presents several applications of the statistics: A new continuous distribution with five parameters—the modified beta Gompertz distribution; A method to calculate the p-value associated with the Anderson–Darling statistic; An approach of repeated measurement designs; A validated model to predict statement mutations score; A new family of structural descriptors, called the extending characteristic polynomial (EChP) family, used to express the link between the structure of a compound and its properties. This collection brings together authors from Europe and Asia with a specific contribution to the knowledge in regards to theoretical and applied statistics.

MaxEnt 2019—Proceedings, 2019, MaxEnt 2019The 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

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ISBN: 9783039284764 9783039284771 Year: Pages: 312 DOI: 10.3390/books978-3-03928-477-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Mathematics
Added to DOAB on : 2020-04-07 23:07:09
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This Proceedings book presents papers from the 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2019. The workshop took place at the Max Planck Institute for Plasma Physics in Garching near Munich, Germany, from 30 June to 5 July 2019, and invited contributions on all aspects of probabilistic inference, including novel techniques, applications, and work that sheds new light on the foundations of inference. Addressed are inverse and uncertainty quantification (UQ) and problems arising from a large variety of applications, such as earth science, astrophysics, material and plasma science, imaging in geophysics and medicine, nondestructive testing, density estimation, remote sensing, Gaussian process (GP) regression, optimal experimental design, data assimilation, and data mining.

Elements of Causal Inference

Authors: --- ---
Book Series: Adaptive Computation and Machine Learning series ISBN: 9780262344296 9780262037310 Year: Pages: 288 Language: English
Publisher: The MIT Press
Subject: Computer Science
Added to DOAB on : 2019-01-17 11:41:31
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A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

The Application of Mathematics to Physics and Nonlinear Science

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ISBN: 9783039287260 / 9783039287277 Year: Pages: 122 DOI: 10.3390/books978-3-03928-727-7 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Mathematics
Added to DOAB on : 2020-06-09 16:38:57
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Nonlinear science is the science of, among other exotic phenomena, unexpected and unpredictable behavior, catastrophes, complex interactions, and significant perturbations. Ocean and atmosphere dynamics, weather, many bodies in interaction, ultra-high intensity excitations, life, formation of natural patterns, and coupled interactions between components or different scales are only a few examples of systems where nonlinear science is necessary. All outstanding, self-sustained, and stable structures in space and time exist and protrude out of a regular linear background of states mainly because they identify themselves from the rest by being highly localized in range, time, configuration, states, and phase spaces. Guessing how high up you drive toward the top of the mountain by compiling your speed, road slope, and trip duration is a linear model, but predicting the occurrence around a turn of a boulder fallen on the road is a nonlinear phenomenon. In an effort to grasp and understand nonlinear phenomena, scientists have developed several mathematical approaches including inverse scattering theory, Backlund and groups of transformations, bilinear method, and several other detailed technical procedures. In this Special Issue, we introduce a few very recent approaches together with their physical meaning and applications. We present here five important papers on waves, unsteady flows, phases separation, ocean dynamics, nonlinear optic, viral dynamics, and the self-appearance of patterns for spatially extended systems, which are problems that have aroused scientists’ interest for decades, yet still cannot be predicted and have their generating mechanism and stability open to debate. The aim of this Special Issue was to present these most debated and interesting topics from nonlinear science for which, despite the existence of highly developed mathematical tools of investigation, there are still fundamental open questions.

Risk Analysis and Portfolio Modelling

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ISBN: 9783039216246 9783039216253 Year: Pages: 224 DOI: 10.3390/books978-3-03921-625-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Business and Management
Added to DOAB on : 2019-12-09 11:49:15
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Financial Risk Measurement is a challenging task, because both the types of risk and the techniques evolve very quickly. This book collects a number of novel contributions to the measurement of financial risk, which address either non-fully explored risks or risk takers, and does so in a wide variety of empirical contexts.

Financial Econometrics

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ISBN: 9783039216260 9783039216277 Year: Pages: 136 DOI: 10.3390/books978-3-03921-627-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Economics
Added to DOAB on : 2019-12-09 11:49:15
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Financial econometrics has developed into a very fruitful and vibrant research area in the last two decades. The availability of good data promotes research in this area, specially aided by online data and high-frequency data. These two characteristics of financial data also create challenges for researchers that are different from classical macro-econometric and micro-econometric problems. This Special Issue is dedicated to research topics that are relevant for analyzing financial data. We have gathered six articles under this theme.

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