rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Advanced Statistical Methods for Astrophysical Probes of Cosmology
  name:
    name part: March, Marisa Cristina.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XX, 180 p. online resource.
  abstract: 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.
  table of contents: 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.
  note: by Marisa Cristina March.
  subject:
    topic: Physics
  subject:
    topic: Observations, Astronomical
  subject:
    topic: Astronomy
    topic: Observations
  subject:
    topic: Cosmology
  subject:
    topic: Statistical physics
  subject:
    topic: Dynamical systems
  subject:
    topic: Statistics
  subject:
    topic: Physics
  subject:
    topic: Cosmology
  subject:
    topic: Astronomy, Observations and Techniques
  subject:
    topic: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  subject:
    topic: Statistical Physics, Dynamical Systems and Complexity
  classification: QB980-991
  classification: 523.1
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Theses, Recognizing Outstanding Ph.D. Research
  identifier: 9783642350603
  identifier: http://dx.doi.org/10.1007/978-3-642-35060-3
  record info:
    record creation date: 130125
    record change date: 20151030111442.0
    record identifier: 978-3-642-35060-3