April 2017 - SIPTA News - lists.sipta.org

Re: [SIPTA] Appropriate forum for IP questions?

by Gert De Cooman

April 7, 2017

Re: [SIPTA] Appropriate forum for IP questions?

by Erik Quaeghebeur

Dear Marshall, > Most of the notices that appear on this list are announcements, so it > doesn’t seem appropriate to ask detailed, non-expert questions here. I > hope you don’t mind if I ask a more meta-level question. > > Is there an appropriate place for asking mathematical IP questions? > Another mailing list? Asking questions here is appropriate, I think. However, there is also a SIPTA Q&A site: <http://sipta.org/qa/>. But I do not know whether it is still maintained. The maintainers read this list, so they'll respond. As I seem to have lost my account details there, I'll give a quick answer to your question here. If needed we can continue on the Q&A site later. > math.stackexchange.com > stats.stackexchange.com > mathoverflow.net I think all three can be appropriate, depending on the type of question. Perhaps the community could decide to use the Stack Exchange sites if maintaining and using its own Q&A site is too burdensome. But then we'd need to create a tag in those and have IP people follow that, or have an automated mail be sent here whenever a new question with the ‘imprecise probability’ tag appears there. Let's wait to see what the SIPTA Q&A maintainers think. > [In case it’s helpful, to illustrate the level of question I want to be > able ask, here is the question with which I’m currently struggling: I’m > reading Hermans and Skulj’s “Stochastic processes” chapter of of > Introduction to Imprecise Probabilities, and I’m puzzled by the novel (to > me) definition of “transition operator” in 11.3. I understand that it’s > intended as a sort of generalization of the standard Markov process > transition operator concept, but there’s something I’m just not seeing > due to lack of experience, lack of insight, etc.: As I understand > definition 11.3, T_n f(x) is essentially a conditional expectation—i.e., > in traditional notation: E( f | X_n = x). How is it that T_n can > operate on f(x) and “see” what x is? Either I must take 11.3 to imply > that the domain of T_n consists of the range of f, or that the domain of > T_n consists of gambles f in L(X_n). Either way, x does not seem to > appear as an argument to T_n; it seems to “know” nothing about x. If the > first interpretation were correct, then if f was not one-to-one, f(x) > could equal f(y) where x != y, and I see no reason to assume that E(f | > X_n=x) must always equal E(f | X_n=y). I’m not sure if I’m making myself > clear; I’m sure that there’s something I've overlooked, or that I’m > misinterpreting some notation or concept.] T_n is an operator that maps gambles to gambles. So T_n f or T_n(f) returns a gamble. For example, g. So T_n f(x) = g(x) = (T_n f)(x) = E(f|X_n=x). So indeed, as you state “the domain of T_n consists of gambles f in L(X_n)”, but also the range of T_n consists of gambles g in L(X_n). Best, Erik -- http://ac.erikquaeghebeur.name

April 7, 2017

Re: [SIPTA] Appropriate forum for IP questions?

by Sébastien Destercke

Dear Abrams, I will first reassure you: this list is intended for discussions of all kinds around IP, so formal questions are more than welcomed. It is just that we did not have some for a while now. As for publishing it on stakexchange forums, it is true that the community is small, but I think the best way to go is to post your question on this list, and also to signal to people that you have posted it in a stackexchange forum that seems the most suited to you, so that people from here as well as roaming these forums can answer. This would also help increase imprecise probability visibility on those forums. What is the most appropriate forum then depends on the question: a question on learning and Bayes update rule would most likely be adapted to st, a question about the mathematics of lower previsions to mathematics, and a question about credal network complexity to computer science. So math, cs and st, depending on your question. I guess your question could go to math. or st. I will let the experts on Markov Chains extensions reply precisely to your technical question. Best Sebastien 2017-04-06 18:46 GMT+02:00 Abrams, Marshall <mabrams(a)uab.edu>: > Most of the notices that appear on this list are announcements, so it > doesn’t seem appropriate to ask detailed, non-expert questions here. I hope > you don’t mind if I ask a more meta-level question. > > Is there an appropriate place for asking mathematical IP questions? Another > mailing list? > > An option that I’ve explored in the past is to ask questions about imprecise > probability on mathematics the question-and-answer site > math.stackexchange.com, but so far this doesn’t seem to be a site in which > people knowledgeable about IP participate. (Questions on math.stackexchange > are classified by “tags”. There is so little interest in IP that I’ve found > no tags representing any aspect of it.) stats.stackexchange.com is > another option, but that’s focused on statistics, and not all IP questions > are statistics-related. mathoverflow.net is part of the same family of > sites, but it’s "a question and answer site for professional > mathematicians.” (Whatever that term means, it would not apply to me.) > > Thanks very much- > > Marshall > > [In case it’s helpful, to illustrate the level of question I want to be able > ask, here is the question with which I’m currently struggling: I’m reading > Hermans and Skulj’s “Stochastic processes” chapter of of Introduction to > Imprecise Probabilities, and I’m puzzled by the novel (to me) definition of > “transition operator” in 11.3. I understand that it’s intended as a sort of > generalization of the standard Markov process transition operator concept, > but there’s something I’m just not seeing due to lack of experience, lack of > insight, etc.: As I understand definition 11.3, T_n f(x) is essentially a > conditional expectation—i.e., in traditional notation: E( f | X_n = x). > How is it that T_n can operate on f(x) and “see” what x is? Either I must > take 11.3 to imply that the domain of T_n consists of the range of f, or > that the domain of T_n consists of gambles f in L(X_n). Either way, x does > not seem to appear as an argument to T_n; it seems to “know” nothing about > x. If the first interpretation were correct, then if f was not one-to-one, > f(x) could equal f(y) where x != y, and I see no reason to assume that E(f | > X_n=x) must always equal E(f | X_n=y). I’m not sure if I’m making myself > clear; I’m sure that there’s something I've overlooked, or that I’m > misinterpreting some notation or concept.] > > Marshall Abrams, Associate Professor > Department of Philosophy, University of Alabama at Birmingham > http://http://members.logical.net/~marshall > Email: mabrams(a)uab.edu; Phone: (205) 996-7483; Fax: (205) 975-6610 > Mail: HB 414A, 900 13th Street South, Birmingham, AL 35294-1260; Office: HB > 418 > > > _______________________________________________ > SIPTA mailing list > SIPTA(a)idsia.ch > http://mailman2.ti-edu.ch/mailman/listinfo/sipta > -- ==================================== Sebastien Destercke, Ph. D. CNRS researcher in computer science. Université de Technologie de Compiegne U.M.R. C.N.R.S. 7253 Heudiasyc Avenue de Landshut F-60205 Compiegne Cedex FRANCE Tel: +33 (0)3 44 23 79 85 Fax: +33 (0)3 44 23 44 77 ====================================

April 7, 2017

[SIPTA] Appropriate forum for IP questions?

by Abrams

April 6, 2017

[SIPTA] Fw: SUM 2017 Deadline Extension -- Scalable Uncertainty Management

by Kreinovich

FYI ________________________________________ From: Serafin Moral <smc(a)decsai.ugr.es> Sent: Thursday, March 30, 2017 5:33 AM To: uai(a)engr.orst.edu Subject: [UAI] SUM 2017 Deadline Extension -- Scalable Uncertainty Management Deadline for submissions has been extended to April 17 --------------------------------------------------------------------------------------------------------------------------- 11th International Conference on Scalable Uncertainty Management (SUM 2017) Call for papers For updated information, please visit the conference web page at: http://idbis.ugr.es/sum2017/ The 11th International Conference on Scalable Uncertainty Management (SUM) will be held in Granada, Spain on October 4-6, 2017. The conference will bring together researchers who are working with imperfect information in fields such as artificial intelligence, databases, data mining, information retrieval, and risk analysis with the aim of fostering collaboration and cross-fertilization of ideas from different communities. An originality of SUM is that it gives a large space to tutorials about a large range of topics related to uncertainty management. Each tutorial provides a 45 minute survey of one of the research areas in the scope of the conference. ******************** Topics of Interest ******************** We solicit papers on the management of large amounts or complex kinds of uncertain, incomplete, or inconsistent information. We are particularly interested in papers that focus on bridging gaps, for instance between different communities, between numerical and symbolic approaches, or between theory and practice. Topics of interest include (but are not limited to): - Imperfect information in databases - Methods for modeling, indexing, and querying uncertain databases - Top-k queries, skyline query processing, and ranking - Approximate, fuzzy query processing - Uncertainty in data integration and exchange - Uncertainty and imprecision in geographic information systems - Probabilistic databases and possibilistic databases? - Data provenance and trust - Data summarization - Very large datasets - Imperfect information in information retrieval and semantic web applications - Approximate schema and ontology matching - Uncertainty in description logics and logic programming - Learning to rank, personalization, and user preferences - Probabilistic language models - Combining vector-space models with symbolic representations - Inductive reasoning for the semantic web - Imperfect information in artificial intelligence - Statistical relational learning, graphical models, probabilistic inference - Argumentation, defeasible reasoning, belief revision - Weighted logics for managing uncertainty - Reasoning with imprecise probability, Dempster-Shafer theory, possibility theory - Approximate reasoning, similarity-based reasoning, analogical reasoning - Planning under uncertainty, reasoning about actions, spatial and temporal reasoning - Incomplete preference specifications - Learning from data - Risk analysis - Aleatory vs. epistemic uncertainty - Uncertainty elicitation methods - Uncertainty propagation methods - Decision analysis methods - Tools for synthesizing results ************************* Submission guidelines ************************* SUM 2017 solicits papers in the following three categories: - Long papers: technical papers reporting original research or survey papers - Short papers: papers reporting promising work-in-progress, system descriptions, position papers on controversial issues, or survey papers providing a synthesis of some current research trends - Extended abstracts of recently published work in a relevant journal or top-tier conference Accepted long and short papers will be published by Springer in the Lecture Notes in Artificial Intelligence series. Authors of an accepted long or short paper will be expected to sign copyright release forms, and one author is expected to give a presentation at the conference. Authors of accepted abstracts will be expected to present their work during the conference, but the extended abstracts will not be published in the LNCS/LNAI proceedings (they will be made available in a separate booklet). Regular research papers should be at most 14 pages (including references, figures and tables). Short papers should be at most 7 pages. Extended abstracts should be at most 2 pages and should reference the originally published work. Submissions must be formatted according to Springer's guidelines for LNCS authors, which can be found athttp://www.springer.de/comp/lncs/authors.html. Each paper is to be submitted electronically as a single PDF file through EasyChair. Papers not respecting the formatting instructions or page limits may be rejected without review. With the exception of extended abstracts, submissions must be unpublished and must not be under submission elsewhere. All submitted papers will be reviewed by the Program Committee on the basis of technical quality, relevance, significance, and clarity. **************** IMPORTANT DATES **************** - Extended deadline for submissions: April 17, 2017 - Notification: May 22, 2017 - Camera-ready copies: June 12, 2017 - Conference: October 4-6, 2017

April 1, 2017