The Experts below are selected from a list of 60 Experts worldwide ranked by ideXlab platform
M. A. Wahab - One of the best experts on this subject based on the ideXlab platform.
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Climate change impacts on fish catch in the world fishing grounds
Climatic Change, 2008Co-Authors: B. K. Biswas, Yu.m. Svirezhev, B. K. Bala, M. A. WahabAbstract:Climate change impacts on fish catch in the major fishing areas in the world oceans using a new method for forecasting of fish catch is presented with Probability Statements. The data on historical behaviour of surface water temperature and fish catches were analyzed and processed to assess the dynamics of spatial temperature distribution and fish catches for the world oceans. An analysis shows that the species diversity of fish catch does not change significantly with time and hence the total fish catch was used as the main dynamic variables, practically without loss of information about the dynamic properties of the system. A predictor was constructed to predict the dynamics of fish catch for new values of four moments for a future temperature distribution and the predictor’s power was estimated with a Probability Statement. Based on the predicted temperatures for the years 2000–2100, the fish catches in the Pacific, Atlantic and Indian Oceans have been predicted with a Probability Statement.
B. K. Biswas - One of the best experts on this subject based on the ideXlab platform.
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Climate change impacts on fish catch in the world fishing grounds
Climatic Change, 2008Co-Authors: B. K. Biswas, Yu.m. Svirezhev, B. K. Bala, M. A. WahabAbstract:Climate change impacts on fish catch in the major fishing areas in the world oceans using a new method for forecasting of fish catch is presented with Probability Statements. The data on historical behaviour of surface water temperature and fish catches were analyzed and processed to assess the dynamics of spatial temperature distribution and fish catches for the world oceans. An analysis shows that the species diversity of fish catch does not change significantly with time and hence the total fish catch was used as the main dynamic variables, practically without loss of information about the dynamic properties of the system. A predictor was constructed to predict the dynamics of fish catch for new values of four moments for a future temperature distribution and the predictor’s power was estimated with a Probability Statement. Based on the predicted temperatures for the years 2000–2100, the fish catches in the Pacific, Atlantic and Indian Oceans have been predicted with a Probability Statement.
Hung T. Nguyen - One of the best experts on this subject based on the ideXlab platform.
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Probability updating using second order probabilities and conditional event algebra
Information Sciences, 1999Co-Authors: I. R. Goodman, Hung T. NguyenAbstract:Abstract The second order Probability technique is a procedure whereby an incompletely specified (finite-argument) Probability function is modeled as a random vector over the associated natural simplex of all possible Probability functions of a fixed common number of arguments. Often, the distribution of the random vector is assumed to be a uniform one, as in the case of Goodman’s treatment of the probabilistic syllogism (or penguin triangle) problem (1998). But, when compatibility with a non-uniform prior distribution is sought, the uniform random vector assumption must be replaced by a different one. In a related, but distinct direction, conditional event algebra is a new mathematical tool which allows the direct incorporation of conditional relations expressed through conditional probabilities with other such conditional relations or unconditional events [I.R. Goodman, H.T. Nguyen, Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems 3 (3) (1995) 247–339; I.R. Goodman, R.P. Mahler, H.T. Nguyen, Mathematics of Data Fusion, Kluwer Academic Publishers, Dordrecht, Holland, 1997]. The “Judy Benjamin” (JB) problem, as originally posed by Van Fraasen [British Journal of Philosophy of Science 32 (1981) 375–379], has been recently addressed by Grove and Halpern (G&H) [Proceedings of the 13th Conference Uncertainty in AI,1997, pp. 208–214], and has elements in it conducive to analysis via both second order Probability and conditional event algebra: The problem involves the issue of how to update a particular event of interest having a known prior Probability – part of a known uniform prior Probability function – upon a known conditional Probability Statement – when the latter is given through a Probability function otherwise unknown and distinct from the prior Probability function. In the problem as stated, the event of interest is disjoint from the antecedent event in the conditional Probability Statement. G&H make use of a trust principle in order to be able to apply, in effect, the second order Probability technique with an associated uniform distribution. G&H’s result differs sharply from that of Van Fraasen, who employs instead a minimal cross-entropy approach to the problem, in that G&H show no change in Probability values between the prior and posterior assessments. This paper considers in a more general context the application of the second order Probability technique, using the Dirichlet family generalization of the uniform distribution over a simplex, in updating an event upon a conditional Probability Statement when no such disjointness as in the JB problem holds and compatibility is sought with a non-uniform prior. When this is specialized to the disjoint event case, even with a non-uniform prior, the independence result of G&H extends to this context. The JB problem is also considered via a conditional event algebra viewpoint – which avoids the trust principle – in conjunction with the second order Probability technique and general agreement with that of G&H’s independence result is established for the case of disjoint event of interest from the antecedent (and consequence) of the conditional form. These relations are also extended to a more general setting.
Henry E Kyburg - One of the best experts on this subject based on the ideXlab platform.
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a two level system of knowledge representation based on epistemic Probability
Philosophical Studies, 1991Co-Authors: Henry E KyburgAbstract:Abstract : A knowledge state is represented by two sets of Statements, rather than one. One set of Statements represents evidence; it corresponds to recorded data, together with general knowledge that is not open to question in the context at hand. We refer to this as the evidential corpus of knowledge. The other set of Statements represents a body of practical certainties, based on the Statements constituting the evidential corpus. It consists of Statements whose probabilities, relative to the evidential corpus, exceed some explicit level determined by the context. Probabilities are assigned to Statements, relative to a body of evidence called evidential corpus. We require statistical knowledge (not just statistical evidence) as a basis for every Probability Statement. Two facts render this constraint acceptable: It doesn't take much statistical data to yield an approximate statistical hypothesis. And if we adopt the principle that Statements known to have the same truth value are to be assigned the same Probability, we may link many Statements to the same statistical foundation.
Yu.m. Svirezhev - One of the best experts on this subject based on the ideXlab platform.
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Climate change impacts on fish catch in the world fishing grounds
Climatic Change, 2008Co-Authors: B. K. Biswas, Yu.m. Svirezhev, B. K. Bala, M. A. WahabAbstract:Climate change impacts on fish catch in the major fishing areas in the world oceans using a new method for forecasting of fish catch is presented with Probability Statements. The data on historical behaviour of surface water temperature and fish catches were analyzed and processed to assess the dynamics of spatial temperature distribution and fish catches for the world oceans. An analysis shows that the species diversity of fish catch does not change significantly with time and hence the total fish catch was used as the main dynamic variables, practically without loss of information about the dynamic properties of the system. A predictor was constructed to predict the dynamics of fish catch for new values of four moments for a future temperature distribution and the predictor’s power was estimated with a Probability Statement. Based on the predicted temperatures for the years 2000–2100, the fish catches in the Pacific, Atlantic and Indian Oceans have been predicted with a Probability Statement.