IDEAS home Printed from https://ideas.repec.org/b/dau/thesis/123456789-12804.html
   My bibliography  Save this book

Contributions computationnelles à la statistique Bayésienne

Editor

Listed:
  • Robert, Christian P.

Author

Listed:
  • Jacob, Pierre E.

Abstract

This thesis presents contributions to the Monte Carlo methodology used in Bayesian statistics. The Bayesian framework is one of the main approaches to statistics and includes a rich methodology to perform inference and model choice. However, as statistical models become more realistic and drift away from the classical assumptions of normality and linearity, computing some of the quantities involved in the statistical analysis becomes a challenge in itself. In particular high-dimensional integrals have to be efficiently approximated, where the integrands can be highly multimodal. Moreover each point-wise evaluation of the integrands can require a lot of computational effort, which results in expensive integration schemes. These integrals are typically approximated using Monte Carlo methods, requiring the ability to sample from general probability distributions. The first chapter of this document explains this motivating context and reviews some of the most generic Monte Carlo techniques. The following chapters aim at improving some of these techniques, at proposing new methods and at analysing their theoretical properties, in the context of sampling from multimodal and computationally expensive probability distributions.

Suggested Citation

  • Jacob, Pierre E., 2012. "Contributions computationnelles à la statistique Bayésienne," Economics Thesis from University Paris Dauphine, Paris Dauphine University, number 123456789/12804 edited by Robert, Christian P., March.
  • Handle: RePEc:dau:thesis:123456789/12804
    Note: dissertation
    as

    Download full text from publisher

    File URL: http://basepub.dauphine.fr/xmlui/bitstream/123456789/12804/1/2012PA090031.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Nicolas Chopin, 2002. "A sequential particle filter method for static models," Biometrika, Biometrika Trust, vol. 89(3), pages 539-552, August.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    bayesian statistics; Monte Carlo methodology;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:dau:thesis:123456789/12804. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alexandre Faure). General contact details of provider: http://edirc.repec.org/data/daup9fr.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.