The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Zdzisław Pawlak - One of the best experts on this subject based on the ideXlab platform.
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A rough set view on Bayes' Theorem
International Journal of Intelligent Systems, 2003Co-Authors: Zdzisław PawlakAbstract:Rough set theory offers new perspective on Bayes' Theorem. The look on Bayes' Theorem offered by rough set theory reveals that any data set (decision table) satisfies the total probability Theorem and Bayes' Theorem. These properties can be used directly to draw conclusions from objective data without referring to subjective prior knowledge and its revision if new evidence is available. Thus, the rough set view on Bayes' Theorem is rather objective in contrast to subjective “classical” interpretation of the Theorem. © 2003 Wiley Periodicals, Inc.
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Decision Algorithms, Bayes’ Theorem and Flow Graphs
Neural Networks and Soft Computing, 2003Co-Authors: Zdzisław PawlakAbstract:The paper concerns some relationships between decision algorithms, Bayes’ Theorem and flow graphs. It is shown it this paper that every decision algorithm reveals probabilistic properties, particularly it satisfies the total probability Theorem and Bayes’ Theorem. This leads to a new look on Bayesian inference methodology, showing that Bayes’ Theorem can be used to reason directly from data without referring to prior and posterior probabilities, inherently associated with Bayesian inference. Besides, a new form of Bayes’ Theorem is introduced, based on the strength of decision rules, which simplifies essentially computations. Moreover it is shown that decision algorithms can be depicted in a form of a flow graph in which flow is ruled by the total probability Theorem and Bayes’ Theorem. This leads to a new class of flow networks, unlike to those introduced by Ford and Fulkerson. Interpretation of flow graphs as a kind of neural network is briefly discussed.
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Bayes’ Theorem — the Rough Set Perspective
Rough Set Theory and Granular Computing, 2003Co-Authors: Zdzisław PawlakAbstract:Rough set theory offers new insight into Bayes’ Theorem. It does not refer either to prior or posterior probabilities, inherently associated with Bayesian reasoning, but reveals some probabilistic structure of the data being analyzed. This property can be used directly to draw conclusions from data.
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AFSS - The Rough Set View on Bayes' Theorem
Advances in Soft Computing — AFSS 2002, 2002Co-Authors: Zdzisław PawlakAbstract:Rough set theory offers new perspective on Bayes' Theorem. The look on Bayes' Theorem offered by rough set theory reveals that any data set (decision table) satisfies total probability Theorem and Bayes' Theorem. These properties can be used directly to draw conclusions from objective data without referring to subjective prior knowledge and its revision if new evidence is available.Thus the rough set view on Bayes' Theorem is rather objective in contrast to subjective "classical" interpretation of the Theorem.
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JSAI Workshops - Bayes' Theorem Revised - The Rough Set View
New Frontiers in Artificial Intelligence, 2001Co-Authors: Zdzisław PawlakAbstract:Rough set theory offers new insight into Bayes' Theorem. The look on Bayes' Theorem offered by rough set theory is completely different from that used in the Bayesian data analysis philosophy. It does not refer either to prior or posterior probabilities, inherently associated with Bayesian reasoning, but it reveals some probabilistic structure of the data being analyzed. It states that any data set (decision table) satisfies total probability Theorem and Bayes' Theorem. This property can be used directly to draw conclusions from data without referring to prior knowledge and its revision if new evidence is available. Thus in the presented approach the only source of knowledge is the data and there is no need to assume that there is any prior knowledge besides the data. We simply look what the data are telling us. Consequently we do not refer to any prior knowledge which is updated after receiving some data.
Zdzislaw Pawlak - One of the best experts on this subject based on the ideXlab platform.
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Bayes Theorem the rough set perspective
2003Co-Authors: Zdzislaw PawlakAbstract:Rough set theory offers new insight into Bayes’ Theorem. It does not refer either to prior or posterior probabilities, inherently associated with Bayesian reasoning, but reveals some probabilistic structure of the data being analyzed. This property can be used directly to draw conclusions from data.
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rough sets decision algorithms and Bayes Theorem
European Journal of Operational Research, 2002Co-Authors: Zdzislaw PawlakAbstract:Abstract Rough set-based data analysis starts from a data table, called an information system. The information system contains data about objects of interest characterized in terms of some attributes. Often we distinguish in the information system condition and decision attributes. Such information system is called a decision table. The decision table describes decisions in terms of conditions that must be satisfied in order to carry out the decision specified in the decision table. With every decision table a set of decision rules, called a decision algorithm, can be associated. It is shown that every decision algorithm reveals some well-known probabilistic properties, in particular it satisfies the total probability Theorem and Bayes' Theorem. These properties give a new method of drawing conclusions from data, without referring to prior and posterior probabilities, inherently associated with Bayesian reasoning.
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new look on Bayes Theorem the rough set outlook ii
2001Co-Authors: Zdzislaw PawlakAbstract:W: G. Hirano, M. Inuiguchi, and S. Tsumoto, editors, Proceedings of International Workshop on Rough Set Theory and Granular Computing (RSTGC 2001), Matshue, Shimane, Japan, 2001, pages 1-8, 2001. (see also Bayes' Theorem Revisited -The Rough Set View, in: T. Terano, T. Nishida, A. Namatame, S. Tsumoto, Y. Ohsawa, T., Washio (eds.) New Frontiers in Artificial Intelligence, Joint JSAI 2001 Workshop Post-Proceedings, LNAI 2253, Springer 2001, 240-250)
Ismael Yaseen Abdulridha Alasadi - One of the best experts on this subject based on the ideXlab platform.
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A Study On Problem Solving Using Bayes Theorem
International Journal of Research, 2016Co-Authors: Ismael Yaseen Abdulridha AlasadiAbstract:The study on understanding of Bayes’ Theorem and to use that knowledge to investigate practical problems in various professional fields. Provides a means for making probability calculations after revising probabilities when obtaining new information in an important phase of probability analysis. When given P(A) and P(ACB), one can calculate P(B/A) by manipulating the information in the Multiplication Rule. However, one could not calculate P(A/B). Similarly, when given P(B) and P(ACB), one can calculate P(A/B) by manipulating the information in the Multiplication Rule. There is where one can now apply Bayes’ Theorem.
Konrad Oexle - One of the best experts on this subject based on the ideXlab platform.
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Useful probability considerations in genetics: the goat problem with tigers and other applications of Bayes' Theorem.
European journal of pediatrics, 2006Co-Authors: Konrad OexleAbstract:Probabilities or risks may change when new information is available. Common sense frequently fails in assessing this change. In such cases, Bayes’ Theorem may be applied. It is easy to derive and has abundant applications in biology and medicine. Some examples of the application of Bayes' Theorem are presented here, such as carrier risk estimation in X-chromosomal disorders, maximal manifestation probability of a dominant trait with unknown penetrance, combination of genetic and non-genetic information, and linkage analysis. The presentation addresses the non-specialist who asks for valid and consistent explanations. The conclusion to be drawn is that Bayes’ Theorem is an accessible and helpful tool for probability calculations in genetics.
Richard D. Morey - One of the best experts on this subject based on the ideXlab platform.
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Teaching Bayes’ Theorem: Strength of Evidence as Predictive Accuracy
The American Statistician, 2018Co-Authors: Jeffrey N. Rouder, Richard D. MoreyAbstract:Although teaching Bayes’ Theorem is popular, the standard approach—targeting posterior distributions of parameters—may be improved. We advocate teaching Bayes’ Theorem in a ratio form where the pos...
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Teaching Bayes' Theorem: Strength of Evidence As Predictive Accuracy
2017Co-Authors: Jeffrey N. Rouder, Richard D. MoreyAbstract:Although teaching Bayes' Theorem is popular, the standard approach---targeting posterior distributions of parameters---may be improved. We advocate teaching Bayes' Theorem in a ratio form where the posterior beliefs relative to the prior beliefs equals the conditional probability of data relative to the marginal probability of data. This form leads to an interpretation that the strength of evidence is relative predictive accuracy. With this approach, students are encouraged to view Bayes' Theorem as an updating mechanism, to obtain a deeper appreciation of the role of the prior and of marginal data, and to view estimation and model comparison from a unified perspective.
