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Libros sobre el tema "Variational Infernce"

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Autor: Grafiati

Publicado: 7 de julio de 2024

Última modificación: 7 de julio de 2024

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1

Quah, Danny. Exploiting cross section variation for unit root inference in dynamic data. London: London School of Economics, Financial Markets Group, 1994.

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2

Quah, Danny. Exploiting cross section variation for unit root inference in dynamic data. Stockholm: Stockholm University, Institute for International Economic Studies, 1993.

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3

Bartholomew, David J. Statistics without Mathematics. London, UK: SAGE Publications Ltd, 2015.

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4

United States. National Aeronautics and Space Administration., ed. Compositional variation in Apollo 16 impact-melt breccias and inferences for the geology and bombardment history of the central highlands of the moon. [Washington, DC: National Aeronautics and Space Administration, 1994.

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5

United States. National Aeronautics and Space Administration., ed. Compositional variation in Apollo 16 impact-melt breccias and inferences for the geology and bombardment history of the central highlands of the moon. [Washington, DC: National Aeronautics and Space Administration, 1994.

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6

Graphical Models, Exponential Families, and Variational Inference. Now Publishers, 2008.

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7

Sekhon, Jasjeet. The Neyman— Rubin Model of Causal Inference and Estimation Via Matching Methods. Editado por Janet M. Box-Steffensmeier, Henry E. Brady y David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0011.

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This article presents a detailed discussion of the Neyman-Rubin model of causal inference. Additionally, it describes under what conditions ‘matching’ approaches can lead to valid inferences, and what kinds of compromises sometimes have to be made with respect to generalizability to ensure valid causal inferences. Moreover, the article summarizes Mill's first three canons and shows the importance of taking chance into account and comparing conditional probabilities when chance variations cannot be ignored. The significance of searching for causal mechanisms is often overestimated by political scientists and this sometimes leads to an underestimate of the importance of comparing conditional probabilities. The search for causal mechanisms is probably especially useful when working with observational data. Machine learning algorithms can be used against the matching problem.

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8

Bortone, Pietro. Language and Nationality: Social Inferences, Cultural Differences, and Linguistic Misconceptions. Bloomsbury Academic & Professional, 2023.

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9

Bortone, Pietro. Language and Nationality: Social Inferences, Cultural Differences, and Linguistic Misconceptions. Bloomsbury Publishing Plc, 2021.

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10

Schadt, Eric E. Network Methods for Elucidating the Complexity of Common Human Diseases. Editado por Dennis S. Charney, Eric J. Nestler, Pamela Sklar y Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0002.

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The life sciences are now a significant contributor to the ever expanding digital universe of data, and stand poised to lead in both the generation of big data and the realization of dramatic benefit from it. We can now score variations in DNA across whole genomes; RNA levels and alternative isoforms, metabolite levels, protein levels, and protein state information across the transcriptome, metabolome and proteome; methylation status across the methylome; and construct extensive protein–protein and protein–DNA interaction maps, all in a comprehensive fashion and at the scale of populations of individuals. This chapter describes a number of analytical approaches aimed at inferring causal relationships among variables in very large-scale datasets by leveraging DNA variation as a systematic perturbation source. The causal inference procedures are also demonstrated to enhance the ability to reconstruct truly predictive, probabilistic causal gene networks that reflect the biological processes underlying complex phenotypes like disease.

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11

Burgess, Stephen y Simon G. Thompson. Mendelian Randomization: Methods for Causal Inference Using Genetic Variants. Taylor & Francis Group, 2021.

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12

Mendelian Randomization: Methods for Causal Inference Using Genetic Variants. Taylor & Francis Group, 2021.

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13

Burgess, Stephen y Simon G. Thompson. Mendelian Randomization: Methods for Causal Inference Using Genetic Variants. Taylor & Francis Group, 2021.

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14

Chappell, Michael, Bradley MacIntosh y Thomas Okell. Kinetic Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198793816.003.0004.

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The quantification of perfusion from arterial spin labeling (ASL) perfusion MRI data relies upon the principles of tracer kinetics. This chapter first outlines the simplest form of a tracer kinetic model that can be applied to ASL data, before exploring variations on this model that can be applied to extract other hemodynamic information such as arterial transit time. Finally, the chapter examines how tracer kinetic models are used with data to estimate perfusion parameters, including the use of model fitting and Bayesian inference.

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15

Sprenger, Jan y Stephan Hartmann. Bayesian Philosophy of Science. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780199672110.001.0001.

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“Bayesian Philosophy of Science” addresses classical topics in philosophy of science, using a single key concept—degrees of beliefs—in order to explain and to elucidate manifold aspects of scientific reasoning. The basic idea is that the value of convincing evidence, good explanations, intertheoretic reduction, and so on, can all be captured by the effect it has on our degrees of belief. This idea is elaborated as a cycle of variations about the theme of representing rational degrees of belief by means of subjective probabilities, and changing them by a particular rule (Bayesian Conditionalization). Partly, the book is committed to the Carnapian tradition of explicating essential concepts in scientific reasoning using Bayesian models (e.g., degree of confirmation, causal strength, explanatory power). Partly, it develops new solutions to old problems such as learning conditional evidence and updating on old evidence, and it models important argument schemes in science such as the No Alternatives Argument, the No Miracles Argument or Inference to the Best Explanation. Finally, it is explained how Bayesian inference in scientific applications—above all, statistics—can be squared with the demands of practitioners and how a subjective school of inference can make claims to scientific objectivity. The book integrates conceptual analysis, formal models, simulations, case studies and empirical findings in an attempt to lead the way for 21th century philosophy of science.

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16

Liang, Percy, Michael Jordan y Dan Klein. Probabilistic grammars and hierarchical Dirichlet processes. Editado por Anthony O'Hagan y Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.27.

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This article focuses on the use of probabilistic context-free grammars (PCFGs) in natural language processing involving a large-scale natural language parsing task. It describes detailed, highly-structured Bayesian modelling in which model dimension and complexity responds naturally to observed data. The framework, termed hierarchical Dirichlet process probabilistic context-free grammar (HDP-PCFG), involves structured hierarchical Dirichlet process modelling and customized model fitting via variational methods to address the problem of syntactic parsing and the underlying problems of grammar induction and grammar refinement. The central object of study is the parse tree, which can be used to describe a substantial amount of the syntactic structure and relational semantics of natural language sentences. The article first provides an overview of the formal probabilistic specification of the HDP-PCFG, algorithms for posterior inference under the HDP-PCFG, and experiments on grammar learning run on the Wall Street Journal portion of the Penn Treebank.

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17

Volpi, Frédéric. Conclusion. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190642921.003.0007.

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In the four North African countries, the early process of mobilization in protest events illustrated the contingent dynamics of events-generated events. The transformation of localized episodes of unrest into nationwide waves of unrest was not only the product of the strategic and coincidental actions of the protesters but also of the responses of the authoritarian systems in place. The different trajectories of change in the four polities can be used as counterfactuals to map varied scenarios of interactions between multiple players and to draw inferences. They illustrate how the variations in the sequencing of events, formation of arenas of contestation and construction of actors and practices shaped differently the outcomes of the uprisings in each state. Rather than stressing how these transformations are likely outcomes of pre-existing structural trends and tensions, an event-oriented account of the Arab uprisings illustrates instead how contingent these institutional re-articulations were.

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