article · 1994

Hierarchical Probability Estimation

Cited by 0Abstract and details only

Abstract

Estimating probabilities based on measured numbers of occurrences of events provides a central link from probability theory to real world applications. In an important class of applications the probabilistic events correspond to the digitized outcome of an analog sensor. This paper shows theoretically and experimentally that such events are governed by a natural similarity relation which imposes subtle but highly effective a priori constraints on the meta-probability distribution, i.e. the probability distribution of probability values. The application of these constraints significantly improves the probability estimates based on the measurements. The results are applied to estimating order-1 Markov model parameters in image processing applications.

Cite this paper

APA 7

Bichsel, M., & Ohnesorge, K. W. (1994). Hierarchical Probability Estimation. https://doi.org/10.5244/c.8.15

MLA 9

Bichsel, Martin, and Krystyna W. Ohnesorge. “Hierarchical Probability Estimation.” 1994, pp. 15.1–15.10, https://doi.org/10.5244/c.8.15.

Chicago

Bichsel, Martin, and Krystyna W. Ohnesorge. 1994. “Hierarchical Probability Estimation.” https://doi.org/10.5244/c.8.15.

Harvard

Bichsel, M. and Ohnesorge, K.W. (1994) ‘Hierarchical Probability Estimation’. Available at: https://doi.org/10.5244/c.8.15.

BibTeX

@article{bichsel1994hierarchical,
  title = {{Hierarchical Probability Estimation}},
  author = {Bichsel, Martin and Ohnesorge, Krystyna W.},
  pages = {15.1--15.10},
  year = {1994},
  doi = {10.5244/c.8.15}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.