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Advanced statistical methods for astrophysical probes of cosmology

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.

Ketersediaan
#
Perpustakaan Pusat E1752
E1752
Tersedia
Informasi Detail
Judul Seri
Springer Theses
No. Panggil
E1752
Penerbit
Heidelberg : Springer Berlin.,
Deskripsi Fisik
xx, 180 hlm.
Bahasa
English
ISBN/ISSN
9783642350603
Klasifikasi
NONE
Tipe Isi
text
Tipe Media
computer
Tipe Pembawa
online resource
Edisi
-
Subjek
Kosmologi (Alam Semesta)
Statistika untuk Teknik
Teknik dan Pengamatan : Astronomi
Statistika untuk Ilmu Komputer
Statistika untuk Fisika
Statistika Fisika dan Sistem Dinamis
Info Detail Spesifik
-
Pernyataan Tanggungjawab
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Tidak tersedia versi lain

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