Detail publikace

On Bayesian Decision-Making and Approximation of Probability Densities

PAPEŽ, M.

Originální název

On Bayesian Decision-Making and Approximation of Probability Densities

Anglický název

On Bayesian Decision-Making and Approximation of Probability Densities

Jazyk

en

Originální abstrakt

An approximation of a true, unknown, posterior probability density (pd) representing some real state-space systém is presented as Bayesian decision-making among a set of possible descriptions (models). The decision problem is carefully defined on its basic elements and it is shown how it leads to the use of the Kullback-Leibler (KL) divergence for evaluating a loss of information between the unknown posterior pd and its approximation. The resulting algorithm is derived on a general level, allowing specific algorithms to be designed according to a selected class of the probability distributions. A concrete example of the algorithm is proposed for the Gaussian case. An experiment is performed assuming that none of the possible descriptions is precisely identical with the unknown system.

Anglický abstrakt

An approximation of a true, unknown, posterior probability density (pd) representing some real state-space systém is presented as Bayesian decision-making among a set of possible descriptions (models). The decision problem is carefully defined on its basic elements and it is shown how it leads to the use of the Kullback-Leibler (KL) divergence for evaluating a loss of information between the unknown posterior pd and its approximation. The resulting algorithm is derived on a general level, allowing specific algorithms to be designed according to a selected class of the probability distributions. A concrete example of the algorithm is proposed for the Gaussian case. An experiment is performed assuming that none of the possible descriptions is precisely identical with the unknown system.

Dokumenty

BibTex


@inproceedings{BUT114323,
  author="Milan {Papež}",
  title="On Bayesian Decision-Making and Approximation of Probability Densities",
  annote="An approximation of a true, unknown, posterior probability density (pd) representing some real state-space systém is presented as Bayesian decision-making among a set of possible descriptions (models). The decision problem is carefully defined on its basic elements and it is shown how it leads to the use of the Kullback-Leibler (KL) divergence for evaluating a loss of information between the unknown posterior pd and its approximation. The resulting algorithm is derived on a general level, allowing specific algorithms to be designed according to a selected class of the probability distributions. A concrete example of the algorithm is proposed for the Gaussian case. An experiment is performed assuming that none of the possible descriptions is precisely identical with the unknown system.",
  address="Institute of Electrical and Electronics Engineers",
  booktitle="38th International Conference on Telecommunications and Signal Processing (TSP)",
  chapter="114323",
  doi="10.1109/TSP.2015.7296313",
  howpublished="electronic, physical medium",
  institution="Institute of Electrical and Electronics Engineers",
  year="2015",
  month="july",
  pages="499--503",
  publisher="Institute of Electrical and Electronics Engineers",
  type="conference paper"
}