Browse Menu >>

Browse Subjects >>

Biomedical & Life Sciences

Business & Economics

Chemistry & Materials Science

Computer Science & Communications

Earth & Environmental Sciences

Engineering

Medicine & Healthcare

Physics & Mathematics

Social Sciences & Humanities

Why Us? >>

  • - Open Access
  • - Peer-reviewed
  • - Rapid publication
  • - Lifetime hosting
  • - Free indexing service
  • - Free promotion service
  • - More citations
  • - Search engine friendly

Free SCIRP Newsletters>>

Add your e-mail address to receive free newsletters from SCIRP.

 

Contact Us >>

WhatsApp  +86 18163351462(WhatsApp)
   
Paper Publishing WeChat
Book Publishing WeChat
(or Email:book@scirp.org)

Article citations

More>>

Pearson, E. (1925) Bayes Theorem, Examined in the Light of Experimental Sampling. Biometrika, 17, 388-442.
https://doi.org/10.1093/biomet/17.3-4.388

has been cited by the following article:

  • TITLE: Use of BayesSim and Smoothing to Enhance Simulation Studies

    AUTHORS: Jeffrey D. Hart

    KEYWORDS: Loss Function, Bayes Risk, Prior Distribution, Regression, Simulation, Skew-Normal Distribution, Goodness of Fit

    JOURNAL NAME: Open Journal of Statistics, Vol.7 No.1, February 28, 2017

    ABSTRACT: The conventional form of statistical simulation proceeds by selecting a few models and generating hundreds or thousands of data sets from each model. This article investigates a different approach, called BayesSim, that generates hundreds or thousands of models from a prior distribution, but only one (or a few) data sets from each model. Suppose that the performance of estimators in a parametric model is of interest. Smoothing methods can be applied to BayesSim output to investigate how estimation error varies as a function of the parameters. In this way inferences about the relative merits of the estimators can be made over essentially the entire parameter space, as opposed to a few parameter configurations as in the conventional approach. Two examples illustrate the methodology: One involving the skew-normal distribution and the other nonparametric goodness-of-fit tests.