Studies in Computational Intelligence
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An Invitation to Quantum Probability Calculus
Studies in Computational Intelligence - - Trang 121-140 - 2021
This paper is about quantum probability for econometricians. The intent is to lay down necessary material on quantum probability calculus to develop associated statistics for economic applications, and not for physics or quantum mechanics.
Types of Interval Data Sets: Towards Feasible Algorithms
Studies in Computational Intelligence - - Trang 79-94 - 2012
Need to consider specific types of interval data sets. The main objective of this book is to compute statistics under interval uncertainty. The simplest and most widely used statistical characteristics are mean and variance. We already know that computing the mean...
Computing under Fuzzy Uncertainty Can Be Reduced to Computing under Interval Uncertainty: Reminder
Studies in Computational Intelligence - - Trang 61-61 - 2012
In this part, we present algorithms for computing the values of different statistical characteristics C(x1,...,x
n
) under interval and fuzzy uncertainty. In Chapter 4, we have explained that the problem of computing these values...
Fuzzy without Fuzzy: Why Fuzzy-Related Aggregation Techniques Are Often Better Even in Situations without True Fuzziness
Studies in Computational Intelligence - - Trang 27-51 - 2009
Fuzzy techniques have been originally invented as a methodology that transforms the knowledge of experts formulated in terms of natural language into a precise computer-implementable form. There are many successful applications of this methodology to situations in...
Computing Statistics under Probabilistic and Interval Uncertainty: A Brief Description
Studies in Computational Intelligence - - Trang 9-10 - 2012
Computing statistics under probabilistic uncertainty. In the case of probabilistic uncertainty, we know the probability distributions for measurement errors corresponding to all the inputs x1,..., x
n
, and we want to find the...
Computing Statistics under Interval Uncertainty: Possibility of Parallelization
Studies in Computational Intelligence - - Trang 221-224 - 2012
In this chapter, we show how the algorithms for estimating variance under interval and fuzzy uncertainty can be parallelized. The results of this chapter first appeared in [336]. Need for parallelization. Traditional algorithms for computing the population variance V...
From Computing Statistics under Interval and Fuzzy Uncertainty to Practical Applications: Need to Propagate the Statistics through Data Processing
Studies in Computational Intelligence - - Trang 251-259 - 2012
Need for data processing. In many areas of science and engineering, we are interested in a quantity y which is difficult (or even impossible) to measure directly. For example, it is difficult to directly measure the distance to a faraway star or the amount of oil in...
Applications to Information Management: How to Estimate Degree of Trust
Studies in Computational Intelligence - - Trang 277-281 - 2012
In this chapter, we use the probabilistic and interval uncertainty to estimate the degree of trust in an agent. Some of these results first appeared in [56, 141].
Results and Algorithms
In the traditional approach to trust, we either...
Computing Outlier Thresholds under Interval Uncertainty
Studies in Computational Intelligence - - Trang 129-151 - 2012
In many application areas, it is important to detect outliers. The traditional engineering approach to outlier detection is that we start with some “normal” values x1,..., x
n
, compute the sample average E, the sample...
Computing Statistics under Fuzzy Uncertainty: Formulation of the Problem
Studies in Computational Intelligence - - Trang 11-17 - 2012
Need to process fuzzy uncertainty. In many practical situations, we only have expert estimates for the inputs x
i
. Sometimes, experts provide guaranteed bounds on the x
i
, and even the...
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