ISIPTA '07 PROGRAMME
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Monday 16 July 2007: Tutorials 

08:00  09:00 
Registration 
09:00  09:45 
Tutorial I (first part) 
09:45  09:50 
Short Break 
09:50  10:35 
Tutorial I (second part) 
10:35  11:00 
Morning Coffee Break 
11:00  11:45 
Tutorial II (first part) 
11:45  11:50 
Short Break 
11:50  12:35 
Tutorial II (second part) 
12:35  14:00 
Lunch Break 
14:00  14:45 
Tutorial III (first part) 
14:45  14:50 
Short Break 
14:50  15:35 
Tutorial III (second part) 
15:35  16:00 
Afternoon Coffee Break 
16:00  16:45 
Tutorial IV (first part) 
16:45  16:50 
Short Break 
16:50  17:35 
Tutorial IV (second part) 
17:45 
Meeting for Guided City Walk 
Tuesday 17 July 2007 

08:30  08:40 
Opening Session 
08:45  10:25 
Statistical Reasoning + Decision making I 
10:25  10:40 
Morning Coffee Break 
10:40  12:00 
Applications 
12:00  13:10 
Morning Poster Session 
13:10  14:30 
Lunch Break 
14:30  16:30 
Coherence and Natural extension 
16:30  16:50 
Afternoon Coffee Break 
16:50  18:00 
Invited Talk I 
18:00  19:00 
Afternoon Poster Session 
20:00 
Meeting for Welcome Party 
20:30  ... 
Welcome Party at Rezava Kotva Restaurant 
Wednesday 18 July 2007 

08:45  10:25 
Markov models + Psychological studies 
10:25  10:40 
Morning Coffee Break 
10:40  12:00 
Foundations 
12:00  13:10 
Morning Poster Session 
13:10  14:30 
Lunch Break 
14:30  16:30 
Belief functions and Random sets 
16:30  16:50 
Afternoon Coffee Break 
16:50  18:00 
Invited talk II 
18:00  19:00 
Afternoon Poster Session 
20:00 
Meeting for Symposium Dinner 
20:15  … 
Symposium Dinner at the Kamenny Most Restaurant 
Thursday 19 July 2007 

08:45  10:25 
Credal nets + Probabilistic Logic 
10:25  10:40 
Morning Coffee Break 
10:40  12:00 
General aspects 
12:00  13:10 
Morning Poster Session 
13:10  14:30 
Lunch Break 
14:30  16:10 
Statistical Reasoning + Decision Making II 
16:10  16:30 
Afternoon Coffee Break 
16:30  17:50 
Predictive inference and Prior ignorance 
17:50  18:50 
Afternoon Poster Session 
18:50  19:00 
Closing Session 
Monday 16 July 2007
TUTORIAL I 09:00  10:35
Risk analysis: rough but ready tools for calculations under variability and uncertainty
Scott Ferson
Risk analysis is widely used in many disciplines to quantify risks or expectations in the face of pervasive variability and profound uncertainty about both natural and engineered systems. Although most analyses today are still based on point estimates, awkward qualitative assessments, or probabilistic calculations employing unwarranted assumptions, the methods of imprecise probability hold great promise for allowing analysts to develop quantitative models that make use of the knowledge and data that are available but do not require untenable or unjustified assumptions or simplifications. This tutorial will introduce some of the methods that are easiest to make calculations with, including probability bounds analysis, DempsterShafer evidence theory, and interval statistics, and will show how they can be used to address the basic problems that risk analysts face: not knowing the input distributions, not knowing their correlations, not being sure about the model itself, or even which variables should be considered. We suggest that these tools constitute a practical uncertainty arithmetic (and logic) that can be widely deployed for lots of applications. Of course, not all problems can be well solved by these relatively crude methods. Examples requiring fuller analyses with the methods of imprecise probability are described.
TUTORIAL II 11:00  12:35
An introduction to the theory of coherent lower previsions
Enrique Miranda
In this tutorial, I introduce the main elements of Peter Walley's theory of coherent lower and upper previsions. I review the notions of avoiding sure loss and coherence, and the representation of coherent assessments in terms of sets of linear previsions and sets of almostdesirable gambles. Then, I turn to the notion of natural extension, and give its expression under any of these three equivalent representations. Finally, I study how to update assessments in a coherent way, presenting the main facts about conditional lower previsions.
TUTORIAL III 14:00  15:35
Generalized information theory
George J. Klir
A research program whose objective is to study the dual concepts of informationbased uncertainty and uncertaintybased information in all their manifestations was introduced in the early 1990s under the name "generalized information theory2 (GIT). The purpose of this tutorial is to introduce conceptual boundaries within which GIT operates and a comprehensive overview of principal results that emerged from GIT. As in classical information theory, uncertainty is the primary concept and information is defined in terms of uncertainty reduction. GIT is based on a twodimensional expansion of classical information theory. In one dimension, additive probability measures of classical information theory are expanded to various types of nonadditive measures. In the other dimension, the theory of classical sets, within which probability measures are formalized, is expanded to the various theories of fuzzy sets. Each choice of a particular set theory and a particular measure theory defines a particular information theory. The full development of any of these information theories requires that issues at each of the following four levels be adequately addressed: (1) an uncertainty function, u, of the theory be formalized in term of appropriate axioms; (2) the calculus for dealing with function u be properly developed; (3) a justifiable functional, U, be determined by which the amount of relevant uncertainty (predictive, prescriptive, diagnostic, etc.) associated with function u is measured; and (4) methodological aspects of the theory be developed by utilizing functional U as a measuring instrument. The tutorial is presented in two parts of approximately the same duration. An overall characterization of GIT is presented in the first part. After a brief overview of classical information theory, a general framework for formalizing uncertainty and the associated uncertaintybased information of any conceivable type is sketched. The various theories of imprecise probabilities that have already been developed within this framework are surveyed and some important unifying principles applying to these theories are introduced. The second part is devoted to the issues of measuring uncertainty and information in the various theories and to the methodological principles based on these measuring capabilities. The tutorial is concluded by a discussion of some open problems in the area of GIT. The tutorial is intended as a gentle introduction to the area of GIT, which is covered in a greater depth in the recent book "Uncertainty and Information: Foundation of Generalized Information Theory" by George J. Klir (WileyInterscience, 2006).
TUTORIAL IV 16:00 – 17:35
Decision theories for imprecise preferences and imprecise probabilities
Teddy Seidenfeld
This tutorial offers an overview of selected alternative decision theories designed for use with IP theory. The review begins with an examination of the kinds of rival decision theories that ensue when each of the familiar axioms of, for instance, AnscombeAumann Horse Lottery theory is relaxed to accommodate normalform IP theory. From this base, further generalizations are considered, including: multiagent decision making, extensive form IP theory, and several interesting considerations that attend problems with infinite decision structures.
Tuesday 17 July 2007
STATISTICAL REASONING AND DECISION MAKING I 08:45  10:25
Coherent choice functions under uncertainty (385)
Teddy Seidenfeld, Mark J. Schervish, Joseph B. Kadane
Finite approximations to coherent choice (425)
Matthias C. M. Troffaes
Luceños' discretization method and its application in decision making under ambiguity (327)
Michael Obermeier, Thomas Augustin
Minimax regret treatment choice with finite samples and missing outcome data (415)
Jörg Stoye
Linear regression analysis under sets of conjugate priors (445)
Gero Walter, Thomas Augustin, Annette Peters
APPLICATIONS 10:40  12:00
Uncertainty analysis in food engineering involving imprecision and randomness (21)
Cédric Baudrit, Arnaud Hélias, Nathalie Perrot
Predicting the next pandemic: An exercise in imprecise hazards (41)
Miķelis Bickis, Uģis Bickis
Imprecise probability methods for sensitivity analysis in engineering (317)
Michael Oberguggenberger, Julian King, Bernhard Schmelzer
Estimating probability distributions by observing betting practices (281)
Caroline Lynch, Donald Barry
COHERENCE AND NATURAL EXTENSION 14:30  16:30
Some results on imprecise conditional prevision assessments (31)
Veronica Biazzo, Angelo Gilio
Some bounds for conditional lower previsions (337)
Renato Pelessoni, Paolo Vicig
Coherence graphs (297)
Enrique Miranda, Marco Zaffalon
Enhancement of natural extension (253)
Igor Kozine, Victor Krymsky
Computing expectations with pboxes: two views of the same problem (435)
Lev Utkin, Sébastien Destercke
Coherence and fuzzy reasoning (165)
Serena Doria
INVITED TALK I 16:50  18:00
Gametheoretic probability: Theory and applications
Glenn Shafer
Wednesday 18 July 2007
MARKOV PROCESSES + PSYCHOLOGICAL STUDIES 08:45  10:25
Multilinear and integer programming for Markov decision processes with imprecise probabilities (395)
Ricardo Shirota Filho, Fabio Gagliardi Cozman, Felipe Werndl Trevizan, Cassio Polpo de Campos, Leliane Nunes de Barros
Regular finite Markov chains with interval probabilities (405)
Damjan Škulj
Conditioning in chaotic probabilities interpreted as a generalized Markov chain (365)
Leandro Chaves Rêgo
Human reasoning with imprecise probabilities: Modus ponens and denying the antecedent (347)
Niki Pfeifer, Gernot D. Kleiter
On the explanatory power of indeterminate probabilities (117)
Horacio Arlo Costa, Jeffrey Helzner
FOUNDATIONS 10:40  13:00
The logical concept of probability: Foundation and interpretation (455)
Kurt Weichselberger
Scoring rules, entropy, and imprecise probabilities (307)
Robert Nau, Victor Richmond Jose, Robert Winkler
On coherent immediate prediction: Connecting two theories of imprecise probability (97)
Gert de Cooman, Filip Hermans
An extension of chaotic probability models to realvalued variables (193)
Pablo I. Fierens
BELIEF FUNCTIONS AND RANDOM SETS 14:30  16:30
An independence concept under plausibility function (287)
Marcello Mastroleo, Barbara Vantaggi
Independence concepts in evidence theory (125)
Inés Couso
Compositional models of belief functions (243)
Radim Jiroušek, Jiřina Vejnarová, Milan Daniel
Constructing predictive belief functions from continuous sample data using confidence bands (11)
Astride Aregui, Thierry Denoeux
Multiparameter models: Probability distributions parameterized by random sets (183)
Thomas Fetz
On various definitions of the variance of a fuzzy random variable (135)
Inés Couso, Didier Dubois, Susana Montes, Luciano Sánchez
INVITED TALK II 16:50  18:00
In the realm of probability: Limits to standard probability
Terrence Fine
Thursday 19 July 2007
CREDAL NETS + PROBABILISTIC LOGIC 08:45  10:25
Credal networks for military identification problems (1)
Alessandro Antonucci, Ralph Brühlmann, Alberto Piatti, Marco Zaffalon
Credal nets with probabilities estimated with an extreme Imprecise Dirichlet Model (57)
Andrés Cano, Manuel Gómez Olmedo, Serafín Moral
Inference in credal networks through integer programming (145)
Cassio Polpo de Campos, Fabio Gagliardi Cozman
Climbing the hills of compiled credal networks (213)
Rolf Haenni
Qualitative and quantitative reasoning in hybrid probabilistic logic programs (375)
Emad Saad
GENERAL ASPECTS 10:40  12:00
Comparative probability orders and the flip relation (67)
Marston Conder, Dominic Searles, Arkadii Slinko
Measuring uncertainty with imprecision indices (47)
Andrey Bronevich, Alexander Lepskiy
Relating practical representations of imprecise probabilities (155)
Sébastien Destercke, Didier Dubois, Éric Chojnacki
On σadditive robust representations of convex risk measures for unbounded financial positions in the
presence of uncertainty about the market model (263)
Volker Krätschmer
STATISTICAL REASONING AND DECISION MAKING II 14:30  16:10
Databased decisions under imprecise probability and least favorable models (203)
Robert Hable
Distributions over expected utilities in decision analysis (175)
Love Ekenberg, Mikael Andersson, Mats Danielson, Aron Larsson
Information processing under imprecise risk with the Hurwicz criterion (233)
JeanYves Jaffray, Meglena Jeleva
Quantilefiltered Bayesian learning for the correlation class (223)
Hermann Held
Updating and testing beliefs: An open version of Bayes' rule (271)
Elmar Kriegler
PREDICTIVE INFERENCE AND PRIOR IGNORANCE 16:30  17:50
Jury size and composition  a predictive approach (87)
Frank P. A. Coolen, Brett Houlding, Steven G. Parkinson
Multinomial nonparametric predictive inference with subcategories (77)
Frank P. A. Coolen, Thomas Augustin
Immediate prediction under exchangeability and representation insensitivity (107)
Gert de Cooman, Enrique Miranda, Erik Quaeghebeur
Learning about a categorical latent variable under prior nearignorance (357)
Alberto Piatti, Marco Zaffalon, Fabio Trojani, Marcus Hutter