https://arxiv.org/abs/math/0212400 close this message arXiv smileybones icon Global Survey In just 3 minutes help us understand how you see arXiv. TAKE SURVEY Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation and member institutions. arxiv logo > math > arXiv:math/0212400 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Mathematics > Numerical Analysis arXiv:math/0212400 (math) [Submitted on 1 Dec 2002] Title:Pattern theory: the mathematics of perception Authors:David Mumford Download PDF Abstract: Is there a mathematical theory underlying intelligence? Control theory addresses the output side, motor control, but the work of the last 30 years has made clear that perception is a matter of Bayesian statistical inference, based on stochastic models of the signals delivered by our senses and the structures in the world producing them. We will start by sketching the simplest such model, the hidden Markov model for speech, and then go on illustrate the complications, mathematical issues and challenges that this has led to. Subjects: Numerical Analysis (math.NA); Statistics Theory (math.ST) MSC classes: 60K35, 82B43 Report number: ICM-2002 Cite as: arXiv:math/0212400 [math.NA] (or arXiv:math/0212400v1 [math.NA] for this version) https://doi.org/10.48550/arXiv.math/0212400 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Proceedings of the ICM, Beijing 2002, vol. 1, 401--422 Submission history From: David Mumford [view email] [v1] Sun, 1 Dec 2002 00:00:00 UTC (358 KB) Full-text links: Download: * PDF * PostScript * Other formats (license) Current browse context: math < prev | next > new | recent | 0212 References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export bibtex citation Loading... Bibtex formatted citation x [loading... ] Data provided by: Bookmark BibSonomy logo Mendeley logo Reddit logo ScienceWISE logo (*) Bibliographic Tools Bibliographic and Citation Tools [ ] Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) [ ] Litmaps Toggle Litmaps (What is Litmaps?) [ ] scite.ai Toggle scite Smart Citations (What are Smart Citations?) ( ) Code, Data, Media Code, Data and Media Associated with this Article [ ] Links to Code Toggle Papers with Code (What is Papers with Code?) [ ] ScienceCast Toggle ScienceCast (What is ScienceCast?) ( ) Demos Demos [ ] Replicate Toggle Replicate (What is Replicate?) [ ] Spaces Toggle Hugging Face Spaces (What is Spaces?) ( ) Related Papers Recommenders and Search Tools [ ] Connected Papers Toggle Connected Papers (What is Connected Papers?) [ ] Core recommender toggle CORE Recommender (What is CORE?) ( ) About arXivLabs 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 and how to get involved. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) * About * Help * Click here to contact arXiv Contact * Click here to subscribe Subscribe * Copyright * Privacy Policy * Web Accessibility Assistance * arXiv Operational Status Get status notifications via email or slack