Combining Soft Computing and Statistical Methods in Data Analysis
Over the last forty years there has been a growing interest to extend probability theory and statistics and to allow for more flexible modelling of imprecision, uncertainty, vagueness and ignorance. The fact that in many real-life situations data uncertainty is not only present in the form of random...
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| Other Authors: | , , , , , , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg :
Springer Berlin Heidelberg,
2010.
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| Series: | Advances in Intelligent and Soft Computing,
77 |
| Subjects: | |
| Online Access: | Full Text via HEAL-Link |
Table of Contents:
- Prior Knowledge in the Classification of Biomedical Data.-Estimation of a Simple Genetic Algorithm Applied to a Laboratory Experiment
- A Comparison of Robust Methods for Pareto Tail Modeling in the Case of Laeken Indicators
- R Code for Hausdorff and Simplex Dispersion Orderings in the 2D Case On Some Confidence Regions to Estimate a Linear Regression
- Model for Interval Data Possibilistic Coding: Error Detection vs. Error Correction . Coherent Correction for Conditional Probability Assessments
- with RInferential Rules for Weak Graphoid.-Fast Factorization of Probability Trees and its Application to Recursive Trees Learning
- Option Pricing in Incomplete Markets Based on Partial Information
- Lorenz Curves of extrema
- Likelihood in a Possibilistic and Probabilistic Context: A Comparison
- Nonparametric Predictive Inference for Order Statistics of Future Observations
- Expected Pair-Wise Comparison of the Outcomes of a Fuzzy Random Variable
- The Behavioral Meaning of the Median Functional Classification and the Random Tukey Depth. Practical Issues
- . On Concordance Measures and Copulas with Fractal Support
- Factorisation Properties of the Strong Product etc. ...
