Soft Methods for Integrated Uncertainty Modelling (eBook)

Jonathan Lawry, Enrique Miranda, Alberto Bugarin, Shoumei Li, Maria Angeles Gil, Przemyslaw Grzegorzewski, Olgierd Hryniewicz (Herausgeber)

eBook Download: PDF

2007 | 2006
X, 413 Seiten
Springer Berlin (Verlag)
978-3-540-34777-4 (ISBN)

Infos

This edited volume is the proceedings of the 2006 International Conference on Soft Methods in Probability and Statistics (SMPS 2006) hosted by the Artificial Intelligence Group at the University of Bristol, between 5-7 September 2006. This is the third of a series of biennial conferences organized in 2002 by the Systems Research Institute from the Polish Academy of Sciences in Warsaw, and in 2004 by the Department of Statistics and Operational Research at the University of Oviedo in Spain. These conferences provide a forum for discussion and research into the fusion of soft methods with probability and statistics, with the ultimate goal of integrated uncertainty modelling in complex systems involving human factors. In addition to probabilistic factors such as measurement error and other random effects, the modelling process often requires us to make qualitative and subject judgments that cannot easily be translated into precise probability values. Such judgments give rise to a number of different types of uncertainty including, fuzziness if they are based on linguistic information, epistemic uncertainty when their reliability is in question, ignorance when they are insufficient to identify or restrict key modelling parameters, imprecision when parameters and probability distributions can only be estimated within certain bounds. Statistical theory has not traditionally been concerned with modelling uncertainty arising in this manner but soft methods, a range of powerful techniques developed within AI, attempt to address those problems where the encoding of subjective information is unavoidable. These are mathematically sound uncertainty modelling methodologies which are complementary to conventional statistics and probability theory. Therefore, a more realistic modelling process providing decision makers with an accurate reflection of the true current state of our knowledge (and ignorance) requires an integrated framework incorporating both probability theory, statistics and soft methods. This fusion motivates innovative research at the interface between computer science (AI), mathematics and systems engineering.

Inhalt

Preface 6
Contents 8
Keynote Papers 12
Generalized Theory of Uncertainty (GTU) – Principal Concepts and Ideas 13
Reasoning with Vague Probability Assessments 15
Soft Methods in Earth Systems Engineering 17
Statistical Data Processing under Interval Uncertainty: Algorithms and Computational Complexity 21
Soft Methods in Statistics and Random Information Systems 37
On Testing Fuzzy Independence 38
Variance Decomposition of Fuzzy Random Variables 46
Fuzzy Histograms and Density Estimation 54
Graded Stochastic Dominance as a Tool for Ranking the Elements of a Poset 62
On Neyman-Pearson Lemma for Crisp, Random and Fuzzy Hypotheses 70
Fuzzy Probability Distributions Induced by Fuzzy Random Vectors 79
On the Identifiability of TSK Additive Fuzzy Rule- Based Models 87
An Asymptotic Test for Symmetry of Random Variables Based on Fuzzy Tools 95
Exploratory Analysis of Random Variables Based on Fuzzifications 103
A Method to Simulate Fuzzy Random Variables 111
Friedman’s Test for Ambiguous and Missing Data 119
Probability of Imprecisely-Valued Random Elements with Applications 127
Measure-Free Martingales with Application to Classical Martingales 128
A Note on Random Upper Semicontinuous Functions 136
Optional Sampling Theorem and Representation of Set- Valued Amart 143
On a Choquet Theorem for Random Upper Semicontinuous Functions 150
A General Law of Large Numbers, with Applications 157
Applications and Modelling of Imprecise Operators 165
Fuzzy Production Planning Model for Automobile Seat Assembling 166
Optimal Selection of Proportional Bounding Quantifiers in Linguistic Data Summarization 175
A Linguistic Quantifier Based Aggregation for a Human Consistent Summarization of Time Series 184
Efficient Evaluation of Similarity Quantified Expressions in the Temporal Domain 192
Imprecise Probability Theory 200
Conditional Lower Previsions for Unbounded Random Quantities 201
Extreme Lower Probabilities 210
Equivalence Between Bayesian and Credal Nets on an Updating Problem 221
Varying Parameter in Classification Based on Imprecise Probabilities 229
Comparing Proportions Data with Few Successes 238
A Unified View of Some Representations of Imprecise Probabilities 246
Possibility, Evidence and Interval Methods 255
Estimating an Uncertain Probability Density 256
Theory of Evidence with Imperfect Information 261
Conditional IF-probability 269
On TwoWays for the Probability Theory on IF-sets 278
A Stratification of Possibilistic Partial Explanations 284
Finite Discrete Time Markov Chains with Interval Probabilities 292
Evidence and Compositionality 300
High Level Fuzzy Labels for Vague Concepts 309
Integrated Uncertainty Modelling in Applications 317
Possibilistic Channels for DNA Word Design 318
Transformation of Possibility Functions in a Climate Model of Intermediate Complexity 327
Fuzzy Logic for Stochastic Modeling 336
A CUSUM Control Chart for Fuzzy Quality Data 345
A Fuzzy Synset-Based Hidden Markov Model for Automatic Text Segmentation 353
Applying Fuzzy Measures for Considering Interaction Effects in Fine Root Dispersal Models 361
Scoring Feature Subsets for Separation Power in Supervised Bayes Classification 370
Interval Random Variables and Their Application in Queueing Systems with Long– Tailed Service Times 379
Online Learning for Fuzzy Bayesian Prediction 390
Index 398

Produkt-Details

• Erscheint lt. Verlag: 8.10.2007
• Reihe/Serie: Advances in Intelligent and Soft Computing
• Reihe/Serie: Advances in Intelligent and Soft Computing
• Zusatzinfo: X, 413 p. 59 illus.
• Verlagsort: Berlin
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Technik
• Schlagworte: Artificial Intelligence • Complex System • Complex Systems • Computer-Aided Design (CAD) • Intelligence • Knowledge • Modeling • Modelling • Operations Research • Probability • Soft Computing • Statistics • Uncertainty
• ISBN-10: 3-540-34777-1 / 3540347771
• ISBN-13: 978-3-540-34777-4 / 9783540347774