- SIPTA News - lists.sipta.org

[SIPTA] Documentation page back online!
by Sébastien Destercke April 9, 2016

April 9, 2016

Dear Siptians, We are happy to let you know that the SIPTA documentation page is back online at the following address: http://www.sipta.org/index.php?id=doc The goal of this page is to provide introductory or review documents about different aspects of imprecise probabilities. We would like to update and complement its current content as much as possible. Any person willing to contribute to this page, either by providing links to interesting (open access) review papers or by writing original contributions is encouraged to do so. Please send your proposals directly to the executive editor (sebastien.destercke(a)hds.utc.fr). With our best regards The executive committee.

1 0

[SIPTA] 9th WPMSIIP 2016 - Durham, 2-6 September 2016
by COOLEN F.P.A. April 8, 2016

April 8, 2016

1 0

[SIPTA] 7th SIPTA Summer School on Imprecise Probability - extended pre-registration deadline
by Matthias Troffaes April 5, 2016

April 5, 2016

[All our apologies for cross-posting.] Seventh school of the Society for Imprecise Probability: Theories and Applications (SIPTA), Aug 29 - Sep 2, 2016, Durham, UK. The pre-registration deadline has been exteded to: Friday 22 April 2016 http://www.maths.dur.ac.uk/users/matthias.troffaes/siptass16/registration.h… Are you interested in modelling severe uncertainty in your applications? Are you worried about the impact of the choice of prior in your Bayesian analysis? Are you interested in prior-data conflict and/or missing data? Do you want to learn how imprecise probability can help you making robust decisions under uncertainty? Then the SIPTA summer school is for you! The Society for Imprecise Probability: Theories and Applications (SIPTA) organizes the 7th edition of its school in Durham, UK, during Aug 29 - Sep 2 2016. The school will be held at the Department of Mathematical Sciences, Durham University, close to the city centre of Durham. The aim of the SIPTA schools is to introduce interested students and researchers to the basics of imprecise probability topics, both theoretical and applied. Some of the best specialists in different aspects of imprecise probabilities lecture, during one week, on the main concepts and techniques in their area of expertise, in a friendly environment favouring interactions between participants. The topics covered this year will provide a general introduction to the latest developments in imprecise probability theory. There will also be a focus on applications of imprecise probabilities in risk analysis in engineering and environmental systems. Students will learn how to deal with situations where information is scarce, vague, or conflicting, and when a unique probability distribution may be hard to identify, from mathematical modelling to statistical inference and practical decision making. A basic understanding of probability and calculus are required (first year university level). A knowledge of Bayesian statistics is an advantage but is not required. Further information concerning registration, as well as a detailed programme, can be found on the website: http://www.maths.dur.ac.uk/users/matthias.troffaes/siptass16/index.html We are looking forward to meeting you in Durham. We also encourage you to circulate this announcement in your networks. Best regards, The organizing committee Matthias Troffaes Frank Coolen Gert de Cooman Olivier Strauss Andrea Wiencierz

1 0

[SIPTA] associate prof. position in Telecom Lille
by klein April 1, 2016

April 1, 2016

1 0

[SIPTA] PGM 2016 Call for Papers (deadline May 20)
by Alessandro Antonucci March 29, 2016

March 29, 2016

PGM 2016 is the eight edition of the International Conference on Probabilistic Graphical Models. It will take place in Lugano (Switzerland) between the 6th and the 9th of September 2016. The conference website is http://pgm.idsia.ch. It welcomes contributions on all aspects of graphical models including probabilistic reasoning, decision making, learning and data mining. Both theoretical and applied contributions are welcome. The deadline for submissions is May 20, 2016. ::: PROCEEDINGS ::: The accepted papers will be published in the proceedings track (http://jmlr.csail.mit.edu/proceedings/) of the Journal of Machine Learning Research (JMLR). Submission instructions and the author's kit are available at http://www2.idsia.ch/cms/pgm/submissions The page limit is 12 pages in the JMLR (single-column) style. Each submission will be double-blind reviewed by three referees. ::: IJAR Special Issue ::: Selected papers will be invited to be extended and then submitted to a special issue of the International Journal of Approximate Reasoning (IJAR). ::: INVITED SPEAKERS ::: We are pleased to have three distinguished invited speakers. Guido Consonni is Professor of Statistics at Università Cattolica del Sacro Cuore, Milan, Italy. His current research topics are in the area of: model selection, graphical models and Bayesian networks, objective Bayesian analysis, models for high-dimensional data, hierarchical models. He is Chair of the Objective Bayes Section of the International Society for Bayesian Analysis (ISBA). Fabio G. Cozman is Professor at Universidade de São Paulo, Brazil. He works with probabilistic reasoning and machine learning, with a special interest in formalisms that extend probability theory. He has served as Program and General Chair of the Conf. on Uncertainty in Artificial Intelligence, Area Chair of the Int. Joint Conf. on Artificial Intelligence, and as Associate Editor of the Journal of Artificial Intelligence Research and of the Journal of Approximate Reasoning, as well as of the Artificial Intelligence Journal. Adnan Darwiche is Professor and former Chairman of the Computer Science Department at UCLA. His research focuses on the theory and practice of symbolic and probabilistic reasoning, with recent applications to machine learning. He served as editor-in-chief of the Journal of Artificial Intelligence Research (JAIR) and is an AAAI fellow. He is also author of "Modeling and Reasoning with Bayesian Networks", published by Cambridge University Press in 2009. ::: TUTORIALS ::: We will have two tutorials. "Computational Complexity of Bayesian Networks and Markov Random Fields", by Cassio P. de Campos, Reader at Queen’s University Belfast, UK, and Johan Kwisthout, senior staff member in the Artificial Intelligence Program at Radboud University, The Netherlands. "Learning Sum-Product Networks", by Nicola Di Mauro, Professor at the University of Bari, Italy, and by Antonio Vergari, PhD student at the University of Bari. This tutorial is an ECCAI invited talk, supported by the ECCAI conference sponsorship program. ::: DEADLINES ::: May 20, 2016: Paper submission deadline June 10, 2016: Author notification June 24, 2016: Camera-ready copy due September 6-9, 2016: Conference _________________________________ Alessandro Antonucci IDSIA Dalle Molle Institute for Artificial Intelligence Via Cantonale (Galleria 2) CH-6928, Manno-Lugano, CH mail: alessandro(a)idsia.ch skype: alessandro.antonucci tel: +41 916108515 web: www.idsia.ch/~alessandro _________________________________

1 0

[SIPTA] Lecturer/Senior Lecturer/Reader/Professor in Data Science and/or Intelligent Autonomous Systems at Queen's U Belfast
by Cassio P. de Campos March 24, 2016

March 24, 2016

Lecturer/Senior Lecturer/Reader/Professor in Data Science and/or Intelligent Autonomous Systems Queen's University Belfast - School of Electronics, Electrical Engineering and Computer Science http://www.jobs.ac.uk/job/ANH172/lecturer-senior-lecturer-reader-professor-… Contract Type: Permanent Anticipated interview date: May/June 2016 Closing date: Monday 18 April 2016 We seek to recruit a highly qualified candidate for an academic post to commence in 2016 or 2017 who can build and grow innovative, mission-led research programmes. The candidate should also demonstrate an ability to inspire students and facilitate motivational learning within core undergraduate and specialist postgraduate curricula. The School is undergoing rapid expansion, as part of major investment from the Faculty of Engineering and Physical Sciences (EPS). Delivering against Queen's Vision2020 and beyond, EPS focuses on solving Global Challenges by enabling new forms of interdisciplinary research and education. The investment of EPS in EEECS capitalises on the establishment of the Institute of Electronics, Communication and Information Technology (ECIT), one of Queens four Global Research Institutes (GRIs). ECIT was awarded a 2015 Queen's Anniversary Prize for Further and Higher Education and is recognised for world-leading research excellence. Further EPS investment includes the establishment of a Pioneer Research Programme (PRP) on Intelligent Autonomous Manufacturing Systems (i-AMS), to support research with high impact potential in this area. We are seeking a candidate who can enhance ECIT's international reputation in the following key disciplines: Data Science & Intelligent Autonomous Systems. An opportunity is available at Lecturer, Senior Lecturer, Reader or Professor level. The successful candidate will benefit from additional resources such as studentships, engineering and commercial support and will have access to state of the art facilities, including a transformative £14 million Computer Science building opening in September 2016. Further details on our key disciplines and other candidate information are available at: http://www.ecit.qub.ac.uk/Aboutus/Recruitment/ Informal enquiries may be directed to Professor Dimitrios S. Nikolopoulos, Head of School of EEECS, email: eeecs-hos(a)qub.ac.uk Anticipated interview date: May/June 2016 Closing date: Monday 18 April 2016 Apply online at www.qub.ac.uk/jobs. For further information or assistance contact the Personnel Department, Queen's University Belfast, BT7 1NN. Telephone (028) 9097 3044 or email on personnel(a)qub.ac.uk. The University is committed to equality of opportunity and to selection on merit. It therefore welcomes applications from all sections of society and particularly welcomes applications from people with a disability.

1 0