000 02853nam a22004575i 4500
001 978-3-642-35060-3
003 DE-He213
005 20140220082859.0
007 cr nn 008mamaa
008 130125s2013 gw | s |||| 0|eng d
020 _a9783642350603
_9978-3-642-35060-3
024 7 _a10.1007/978-3-642-35060-3
_2doi
050 4 _aQB980-991
072 7 _aPHR
_2bicssc
072 7 _aSCI015000
_2bisacsh
082 0 4 _a523.1
_223
100 1 _aMarch, Marisa Cristina.
_eauthor.
245 1 0 _aAdvanced Statistical Methods for Astrophysical Probes of Cosmology
_h[electronic resource] /
_cby Marisa Cristina March.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aXX, 177 p. 46 illus., 11 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053
505 0 _aIntroduction -- 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.
520 _aThis 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.
650 0 _aPhysics.
650 1 4 _aPhysics.
650 2 4 _aCosmology.
650 2 4 _aAstronomy, Observations and Techniques.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aStatistical Physics, Dynamical Systems and Complexity.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642350597
830 0 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-35060-3
912 _aZDB-2-PHA
999 _c97597
_d97597