Skip to main content
CenXiv.org
This website is in trial operation, support us!
We gratefully acknowledge support from all contributors.
Contribute
Donate
cenxiv logo > math > arXiv:1911.00217

Help | Advanced Search

Mathematics > Statistics Theory

arXiv:1911.00217 (math)
[Submitted on 1 Nov 2019 (v1) , last revised 13 Nov 2021 (this version, v2)]

Title: Update of prior probabilities by minimal divergence

Title: 先验概率的最小发散更新

Authors:Jan Naudts
Abstract: The present paper investigates the update of an empirical probability distribution with the results of a new set of observations. The optimal update is obtained by minimizing either the Hellinger distance or the quadratic Bregman divergence. The results obtained by the two methods differ. Updates with information about conditional probabilities are considered as well.
Abstract: 本文研究了用一组新的观测结果更新经验概率分布的问题。通过最小化Hellinger距离或二次Bregman散度获得最优更新。这两种方法所得结果不同。还考虑了有关条件概率信息的更新。
Comments: Small improvements
Subjects: Statistics Theory (math.ST) ; Probability (math.PR); Data Analysis, Statistics and Probability (physics.data-an); Quantum Physics (quant-ph); Methodology (stat.ME)
MSC classes: 62G05
Cite as: arXiv:1911.00217 [math.ST]
  (or arXiv:1911.00217v2 [math.ST] for this version)
  https://doi.org/10.48550/arXiv.1911.00217
arXiv-issued DOI via DataCite
Journal reference: Entropy 23, 1668 (2021)
Related DOI: https://doi.org/10.3390/e23121668
DOI(s) linking to related resources

Submission history

From: Jan Naudts [view email]
[v1] Fri, 1 Nov 2019 06:12:15 UTC (9 KB)
[v2] Sat, 13 Nov 2021 16:23:48 UTC (8 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled
  • View Chinese PDF
  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
math.ST
< prev   |   next >
new | recent | 2019-11
Change to browse by:
math
math.PR
physics
physics.data-an
quant-ph
stat
stat.ME
stat.TH

References & Citations

  • NASA ADS
  • Google Scholar
  • Semantic Scholar
a export BibTeX citation Loading...

BibTeX formatted citation

×
Data provided by:

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender (What is IArxiv?)
  • Author
  • Venue
  • Institution
  • Topic

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
  • About
  • Help
  • contact arXivClick here to contact arXiv Contact
  • subscribe to arXiv mailingsClick here to subscribe Subscribe
  • Copyright
  • Privacy Policy
  • Web Accessibility Assistance
  • arXiv Operational Status
    Get status notifications via email or slack

京ICP备2025123034号