Advanced Statistical Methods for Astrophysical Probes of Cosmology:
Gespeichert in:
| Format: | Elektronisch E-Book |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2013
|
| Schriftenreihe: | Springer Theses, Recognizing Outstanding Ph.D. Research
|
| Schlagworte: | |
| Online-Zugang: | TUM01 UBT01 Volltext Inhaltsverzeichnis Abstract |
| Beschreibung: | Introduction -- Cosmology background -- Dark energy and apparent late time acceleration -- Supernovae Ia -- Statistical techniques -- Bayesian Doubt: Should we doubt the Cosmological Constant? -- Bayesian parameter inference for SNeIa data -- Robustness to Systematic Error for Future Dark Energy Probes -- Summary and Conclusions -- Index. 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. |
| Beschreibung: | 1 Online-Ressource |
| ISBN: | 9783642350603 |
| DOI: | 10.1007/978-3-642-35060-3 |
Internformat
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| 500 | |a 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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| 650 | 4 | |a Cosmology | |
| 650 | 4 | |a Astronomy, Observations and Techniques | |
| 650 | 4 | |a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences | |
| 650 | 4 | |a Statistical Physics, Dynamical Systems and Complexity | |
| 700 | 1 | |a March, Marisa Cristina |e Sonstige |4 oth | |
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Datensatz im Suchindex
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| adam_text | ADVANCED STATISTICAL METHODS FOR ASTROPHYSICAL PROBES OF COSMOLOGY
/ MARCH, MARISA CRISTINA
: 2013
TABLE OF CONTENTS / INHALTSVERZEICHNIS
INTRODUCTION
COSMOLOGY BACKGROUND
DARK ENERGY AND APPARENT LATE TIME ACCELERATION
SUPERNOVAE IA
STATISTICAL TECHNIQUES
BAYESIAN DOUBT: SHOULD WE DOUBT THE COSMOLOGICAL CONSTANT?
BAYESIAN PARAMETER INFERENCE FOR SNEIA DATA
ROBUSTNESS TO SYSTEMATIC ERROR FOR FUTURE DARK ENERGY PROBES
SUMMARY AND CONCLUSIONS
INDEX
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
ADVANCED STATISTICAL METHODS FOR ASTROPHYSICAL PROBES OF COSMOLOGY
/ MARCH, MARISA CRISTINA
: 2013
ABSTRACT / INHALTSTEXT
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
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
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| discipline | Physik |
| doi_str_mv | 10.1007/978-3-642-35060-3 |
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| id | DE-604.BV040751408 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T00:33:09Z |
| institution | BVB |
| isbn | 9783642350603 |
| language | English |
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| spelling | Advanced Statistical Methods for Astrophysical Probes of Cosmology by Marisa Cristina March Berlin, Heidelberg Springer Berlin Heidelberg 2013 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Springer Theses, Recognizing Outstanding Ph.D. Research Introduction -- Cosmology background -- Dark energy and apparent late time acceleration -- Supernovae Ia -- Statistical techniques -- Bayesian Doubt: Should we doubt the Cosmological Constant? -- Bayesian parameter inference for SNeIa data -- Robustness to Systematic Error for Future Dark Energy Probes -- Summary and Conclusions -- Index. 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. Physics Cosmology Astronomy, Observations and Techniques Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Statistical Physics, Dynamical Systems and Complexity March, Marisa Cristina Sonstige oth https://doi.org/10.1007/978-3-642-35060-3 Verlag Volltext Springer Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025731160&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Springer Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025731160&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Abstract |
| spellingShingle | Advanced Statistical Methods for Astrophysical Probes of Cosmology Physics Cosmology Astronomy, Observations and Techniques Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Statistical Physics, Dynamical Systems and Complexity |
| title | Advanced Statistical Methods for Astrophysical Probes of Cosmology |
| title_auth | Advanced Statistical Methods for Astrophysical Probes of Cosmology |
| title_exact_search | Advanced Statistical Methods for Astrophysical Probes of Cosmology |
| title_full | Advanced Statistical Methods for Astrophysical Probes of Cosmology by Marisa Cristina March |
| title_fullStr | Advanced Statistical Methods for Astrophysical Probes of Cosmology by Marisa Cristina March |
| title_full_unstemmed | Advanced Statistical Methods for Astrophysical Probes of Cosmology by Marisa Cristina March |
| title_short | Advanced Statistical Methods for Astrophysical Probes of Cosmology |
| title_sort | advanced statistical methods for astrophysical probes of cosmology |
| topic | Physics Cosmology Astronomy, Observations and Techniques Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Statistical Physics, Dynamical Systems and Complexity |
| topic_facet | Physics Cosmology Astronomy, Observations and Techniques Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Statistical Physics, Dynamical Systems and Complexity |
| url | https://doi.org/10.1007/978-3-642-35060-3 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025731160&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025731160&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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