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Geometric constructions with discretized random variables. (English) Zbl 1097.65018

A multivariate generalization of thicket construction is considered. A thicket in \(\mathbb{R}^d\) is a discretized representation of a random vector distribution in the form of finite set \(T=\{(C_i,p_i)\}\), where \(C_i\) are open convex sets in \(\mathbb{R}^d\) and \(p_i\) are probabilities with \(\sum p_i=1\). Lower \(\underline{p}\) and upper \(\overline{p}\) probabilities for \(T\) are defined by \[ \underline{p}(X)=\sum_{C_i\subset X}p_i\;\;\;\overline{p}(X)=\sum_{C_i\cap X\not=\emptyset} p_i. \] A random variable \(\xi\) is represented by a thicket \(T\) if \(\underline{p}(X)\leq \mathbf{P}\{\xi\in X\}\leq\overline{p}(X)\). Algorithms for constructing a thicket with given lower and upper probabilities are described. The authors introduce the concept of nested thicket representation for r.v.s which minimize the information loss. An algorithm is described for construction of a thicket \(T\) which represents \(\xi *\eta\) if representations for \(\xi\) and \(\eta\) are given. Here \(*\) is any binary operation.

MSC:

65C50 Other computational problems in probability (MSC2010)
60E05 Probability distributions: general theory
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[1] Berleant, D. and Goodman-Strauss, C.: Bounding the Results of Arithmetic Operations on Random Variables of Unknown Dependency Using Intervals, Reliable Computing 4 (2) (1998), pp. 147–165. · Zbl 0908.65139
[2] Berleant, D., Xie, L., and Zhang, J.: Stattool: A Tool for Distribution Envelope Determination (DEnv), an Interval-Based Algorithm for Arithmetic Operations on Random Variables, Reliable Computing 9 (2) (2003), pp. 91–108. · Zbl 1020.65010
[3] Berleant, D. and Zhang, J.: Representation and Problem Solving with Distribution Envelope Determination, Reliability Engineering and System Safety 85 (1–3) (2004), pp. 153–168.
[4] Berleant, D. and Zhang, J.: Using Pearson Correlation to Improve Envelopes around the Distributions of Functions, Reliable Computing 10 (2) (2004), pp. 139–161. · Zbl 1036.60010
[5] Hu, S.-M. and Wallner, J.: Error Propagation through Geometric Transformations, J. Geom. Graphics 8 (2) (2004), pp. 171–183. · Zbl 1076.51009
[6] Karloff, H.: Linear Programming, Birkhäuser, Boston, 1991. · Zbl 0748.90040
[7] Pottmann, H., Odehnal, B., Peternell, M., Wallner, J., and Ait Haddou, R.: On Optimal Tolerancing in Computer-Aided Design, in: Martin, R. and Wang, W. (eds), Geometric Modeling and Processing 2000, IEEE Computer Society, Los Alamitos, 2000, pp. 347–363.
[8] Regan, H. A., Ferson, S., and Berleant, D.: Equivalence of Methods for Uncertainty Propagation of Real-Valued Random Variables, Internat. J. Approx. Reason. 36 (2004), pp. 1–30. · Zbl 1095.68118
[9] Requicha, A. A. G.: Towards a Theory of Geometric Tolerancing, Int. J. of Robotics Research 2 (1983), pp. 45–60.
[10] Walley, P.: Measures of Uncertainty in Expert Systems, Artificial Intelligence 83 (1996), pp. 1–58.
[11] Wallner, J., Krasauskas, R., and Pottmann, H.: Error Propagation in Geometric Constructions, Computer-Aided Design 32 (2000), pp. 631–641. · Zbl 05860768
[12] Wallner, J., Schröcker, H.-P., and Hu, S.-M.: Tolerances in Geometric Constraint Problems, Reliable Computing 11 (3) (2005), pp. 235–251. · Zbl 1075.65025
[13] Williamson, R. C. and Downs, T.: Probabilistic Arithmetic. I. Numerical Methods for Calculating Convolutions and Dependency Bounds, Internat. J. Approx. Reason. 4 (2) (1990), pp. 89–158. · Zbl 0703.65100
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