Combination of Upper and Lower Probabilities

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📝 Original Info

  • Title: Combination of Upper and Lower Probabilities
  • ArXiv ID: 1303.5710
  • Date: 2013-03-26
  • Authors: Researchers from original ArXiv paper

📝 Abstract

In this paper, we consider several types of information and methods of combination associated with incomplete probabilistic systems. We discriminate between 'a priori' and evidential information. The former one is a description of the whole population, the latest is a restriction based on observations for a particular case. Then, we propose different combination methods for each one of them. We also consider conditioning as the heterogeneous combination of 'a priori' and evidential information. The evidential information is represented as a convex set of likelihood functions. These will have an associated possibility distribution with behavior according to classical Possibility Theory.

💡 Deep Analysis

Deep Dive into Combination of Upper and Lower Probabilities.

In this paper, we consider several types of information and methods of combination associated with incomplete probabilistic systems. We discriminate between ‘a priori’ and evidential information. The former one is a description of the whole population, the latest is a restriction based on observations for a particular case. Then, we propose different combination methods for each one of them. We also consider conditioning as the heterogeneous combination of ‘a priori’ and evidential information. The evidential information is represented as a convex set of likelihood functions. These will have an associated possibility distribution with behavior according to classical Possibility Theory.

📄 Full Content

In this paper, we consider several types of information and methods of combination associated with incomplete probabilistic systems. We discriminate between 'a priori' and evidential information. The former one is a description of the whole population, the latest is a restriction based on observations for a particular case. Then, we propose different combination methods for each one of them. We also consider conditioning as the heterogeneous combination of 'a priori' and evidential information. The evidential information is represented as a convex set of likelihood functions. These will have an associated possibility distribution with behavior according to classical Possibility Theory.

Reference

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