Bayesian Methods in Cosmology

Author: Michael P. Hobson

Publisher: Cambridge University Press

ISBN: 0521887941

Category: Mathematics

Page: 303

View: 9513

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Comprehensive introduction to Bayesian methods in cosmological studies, for graduate students and researchers in cosmology, astrophysics and applied statistics.
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Open Questions in Cosmology

Author: Gonzalo J. Olmo

Publisher: BoD – Books on Demand

ISBN: 9535108808

Category: Science

Page: 344

View: 1567

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In the last years we have witnessed how the field of Cosmology has experienced a metamorphosis. From being essentially the search for three numbers (the expansion rate, the deceleration parameter, and the cosmological constant), it has become a precision science. This scientific discipline is determined to unravel the most minute details of the elementary processes that took place during the most primitive stages of the Universe and also of the mechanisms driving the cosmic expansion and the growth of structures at the largest scales. To achieve these goals one needs not only the development of new experimental and observational techniques but also a deep understanding of the underlying theoretical frameworks. This book gathers the work of leading experts in these fields and provides a broad view of some of the most relevant open questions faced by Cosmology at the beginning of the twenty-first century.
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Statistical Problems in Particle Physics, Astrophysics and Cosmology

PHYSTAT05, Oxford, UK, 12-15 September 2005

Author: Louis Lyons,Mge Karag”z šnel

Publisher: Imperial College Press

ISBN: 1860946496

Category: Science

Page: 310

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These proceedings comprise current statistical issues in analyzing data in particle physics, astrophysics and cosmology, as discussed at the PHYSTAT05 conference in Oxford. This is a continuation of the popular PHYSTAT series; previous meetings were held at CERN (2000), Fermilab (2000), Durham (2002) and Stanford (2003).In-depth discussions on topical issues are presented by leading statisticians and research workers in their relevant fields. Included are invited reviews and contributed research papers presenting the latest, state-of-the-art techniques.
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Cosmology in Scalar-Tensor Gravity

Author: Valerio Faraoni

Publisher: Springer Science & Business Media

ISBN: 1402019890

Category: Science

Page: 274

View: 7324

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Cosmology in Scalar-Tensor Gravity covers all aspects of cosmology in scalar-tensor theories of gravity. Considerable progress has been made in this exciting area of physics and this book is the first to provide a critical overview of the research. Among the topics treated are: -Scalar-tensor gravity and its limit to general relativity, -Effective energy-momentum tensors and conformal frames, -Gravitational waves in scalar-tensor cosmology, -Specific scalar-tensor theories, -Exact cosmological solutions and cosmological perturbations, -Scalar-tensor scenarios of the early universe and inflation, -Scalar-tensor models of quintessence in the present universe and their far-reaching consequences for the ultimate fate of the cosmos.
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Advanced Statistical Methods for Astrophysical Probes of Cosmology

Author: Marisa Cristina March

Publisher: Springer Science & Business Media

ISBN: 3642350607

Category: Science

Page: 180

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This thesis explores advanced Bayesian statistical methods for extracting key information for cosmological model selection, parameter inference and forecasting from astrophysical observations. Bayesian model selection provides a measure of how good models in a set are relative to each other - but what if the best model is missing and not included in the set? Bayesian Doubt is an approach which addresses this problem and seeks to deliver an absolute rather than a relative measure of how good a model is. Supernovae type Ia were the first astrophysical observations to indicate the late time acceleration of the Universe - this work presents a detailed Bayesian Hierarchical Model to infer the cosmological parameters (in particular dark energy) from observations of these supernovae type Ia.
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Bayesian Astrophysics

Author: Andrés Asensio Ramos,Íñigo Arregui

Publisher: Cambridge University Press

ISBN: 1107102138

Category: Science

Page: 208

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Provides an overview of the fundamentals of Bayesian inference and its applications within astrophysics, for graduate students and researchers.
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Data Analysis in Cosmology

Author: Vicent J. Martinez,Enn Saar,Enrique Martinez Gonzales,Maria Jesus Pons-Borderia

Publisher: Springer Science & Business Media

ISBN: 3540239723

Category: Science

Page: 636

View: 8691

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The amount of cosmological data has dramatically increased in the past decades due to an unprecedented development of telescopes, detectors and satellites. Efficiently handling and analysing new data of the order of terabytes per day requires not only computer power to be processed but also the development of sophisticated algorithms and pipelines. Aiming at students and researchers the lecture notes in this volume explain in pedagogical manner the best techniques used to extract information from cosmological data, as well as reliable methods that should help us improve our view of the universe.
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Issues in Astronomy and Astrophysics: 2011 Edition

Author: N.A

Publisher: ScholarlyEditions

ISBN: 1464963681

Category: Science

Page: 2979

View: 8912

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Issues in Astronomy and Astrophysics / 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Astronomy and Astrophysics. The editors have built Issues in Astronomy and Astrophysics: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Astronomy and Astrophysics in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Astronomy and Astrophysics: 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.
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Bayesian Inference and Maximum Entropy Methods in Science and Engineering

MaxEnt 37, Jarinu, Brazil, July 09–14, 2017

Author: Adriano Polpo,Julio Stern,Francisco Louzada,Rafael Izbicki,Hellinton Takada

Publisher: Springer

ISBN: 3319911430

Category: Mathematics

Page: 304

View: 8277

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These proceedings from the 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2017), held in São Carlos, Brazil, aim to expand the available research on Bayesian methods and promote their application in the scientific community. They gather research from scholars in many different fields who use inductive statistics methods and focus on the foundations of the Bayesian paradigm, their comparison to objectivistic or frequentist statistics counterparts, and their appropriate applications. Interest in the foundations of inductive statistics has been growing with the increasing availability of Bayesian methodological alternatives, and scientists now face much more difficult choices in finding the optimal methods to apply to their problems. By carefully examining and discussing the relevant foundations, the scientific community can avoid applying Bayesian methods on a merely ad hoc basis. For over 35 years, the MaxEnt workshops have explored the use of Bayesian and Maximum Entropy methods in scientific and engineering application contexts. The workshops welcome contributions on all aspects of probabilistic inference, including novel techniques and applications, and work that sheds new light on the foundations of inference. Areas of application in these workshops include astronomy and astrophysics, chemistry, communications theory, cosmology, climate studies, earth science, fluid mechanics, genetics, geophysics, machine learning, materials science, medical imaging, nanoscience, source separation, thermodynamics (equilibrium and non-equilibrium), particle physics, plasma physics, quantum mechanics, robotics, and the social sciences. Bayesian computational techniques such as Markov chain Monte Carlo sampling are also regular topics, as are approximate inferential methods. Foundational issues involving probability theory and information theory, as well as novel applications of inference to illuminate the foundations of physical theories, are also of keen interest.
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