IDEAS home Printed from https://ideas.repec.org/a/bpj/mcmeap/v24y2018i3p203-214n5.html
   My bibliography  Save this article

Markov-Chain Monte-Carlo methods and non-identifiabilities

Author

Listed:
  • Müller Christian

    (RwtH Aachen Joint Research Center for Computational Biomedicine, Aachen, Germany)

  • Weysser Fabian
  • Mrziglod Thomas

    (Bayer AG, Applied Mathematics, Leverkusen, Germany)

  • Schuppert Andreas

    (RwtH Aachen Joint Research Center for Computational Biomedicine, Aachen, Germany)

Abstract

We consider the problem of sampling from high-dimensional likelihood functions with large amounts of non-identifiabilities via Markov-Chain Monte-Carlo algorithms. Non-identifiabilities are problematic for commonly used proposal densities, leading to a low effective sample size. To address this problem, we introduce a regularization method using an artificial prior, which restricts non-identifiable parts of the likelihood function. This enables us to sample the posterior using common MCMC methods more efficiently. We demonstrate this with three MCMC methods on a likelihood based on a complex, high-dimensional blood coagulation model and a single series of measurements. By using the approximation of the artificial prior for the non-identifiable directions, we obtain a sample quality criterion. Unlike other sample quality criteria, it is valid even for short chain lengths. We use the criterion to compare the following three MCMC variants: The Random Walk Metropolis Hastings, the Adaptive Metropolis Hastings and the Metropolis adjusted Langevin algorithm.

Suggested Citation

  • Müller Christian & Weysser Fabian & Mrziglod Thomas & Schuppert Andreas, 2018. "Markov-Chain Monte-Carlo methods and non-identifiabilities," Monte Carlo Methods and Applications, De Gruyter, vol. 24(3), pages 203-214, September.
    • Handle: RePEc:bpj:mcmeap:v:24:y:2018:i:3:p:203-214:n:5
    DOI: 10.1515/mcma-2018-0018
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/mcma-2018-0018
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/mcma-2018-0018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Markus Krauss & Kai Tappe & Andreas Schuppert & Lars Kuepfer & Linus Goerlitz, 2015. "Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Keywords

      Markov-Chain Monte-Carlo; non-identifiability; 65C05;
      All these keywords.

      JEL classification:

      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:bpj:mcmeap:v:24:y:2018:i:3:p:203-214:n:5. See general information about how to correct material in RePEc.

      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 bibliographic 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.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

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

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.