• Corpus ID: 88515503

Introducing Inner Nested Sampling

@article{Erp2017IntroducingIN,
  title={Introducing Inner Nested Sampling},
  author={H. R. Noel van Erp and Ronald. O. Linger and Pieter H. A. J. M. van Gelder},
  journal={arXiv: Methodology},
  year={2017}
}
In this paper we will give a Monte Carlo algorithm by which the moments of a functions of Dirichlet probability distributions can be estimated. This algorithm is called Inner Nested Sampling and is an implementation of Skilling's general Nested Sampling framework. 

References

SHOWING 1-5 OF 5 REFERENCES
The Canvas of Rationality
Kevin Knuth’s applications of lattice theory lead to a simpler, clearer and wider view of the foundations of rational inference. The standard formulas of measure, probability and entropy now rest on
Nested sampling for general Bayesian computation
Nested sampling estimates directly how the likelihood function relates to prior mass. The evidence (alternatively the marginal likelihood, marginal den- sity of the data, or the prior predictive) is
Information Theory, Inference, and Learning Algorithms
  • D. Mackay
  • Computer Science
    IEEE Transactions on Information Theory
  • 2004
Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.
An Introduction to Bayesian Inference in Econometrics
Remarks on Inference in Economics. Principles of Bayesian Analysis with Selected Applications. The Univariate Normal Linear Regression Model. Special Problems in Regression Analysis. On Errors in the
Nested Sampling, In Bayesian Inference and Maximum Entropy Methods in Science and Engineering
  • AIP Conference Proceedings, American Institute of Physics, New-York
  • 2004