12 resultados para Markov decision processes

em Bulgarian Digital Mathematics Library at IMI-BAS


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2010 Mathematics Subject Classification: 60J80.

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2000 Mathematics Subject Classification: 60J80, 60K05.

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Similar to classic Signal Detection Theory (SDT), recent optimal Binary Signal Detection Theory (BSDT) and based on it Neural Network Assembly Memory Model (NNAMM) can successfully reproduce Receiver Operating Characteristic (ROC) curves although BSDT/NNAMM parameters (intensity of cue and neuron threshold) and classic SDT parameters (perception distance and response bias) are essentially different. In present work BSDT/NNAMM optimal likelihood and posterior probabilities are analytically analyzed and used to generate ROCs and modified (posterior) mROCs, optimal overall likelihood and posterior. It is shown that for the description of basic discrimination experiments in psychophysics within the BSDT a ‘neural space’ can be introduced where sensory stimuli as neural codes are represented and decision processes are defined, the BSDT’s isobias curves can simultaneously be interpreted as universal psychometric functions satisfying the Neyman-Pearson objective, the just noticeable difference (jnd) can be defined and interpreted as an atom of experience, and near-neutral values of biases are observers’ natural choice. The uniformity or no-priming hypotheses, concerning the ‘in-mind’ distribution of false-alarm probabilities during ROC or overall probability estimations, is introduced. The BSDT’s and classic SDT’s sensitivity, bias, their ROC and decision spaces are compared.

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2000 Mathematics Subject Classification: 60K15, 60K20, 60G20,60J75, 60J80, 60J85, 60-08, 90B15.

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This paper aims at development of procedures and algorithms for application of artificial intelligence tools to acquire process and analyze various types of knowledge. The proposed environment integrates techniques of knowledge and decision process modeling such as neural networks and fuzzy logic-based reasoning methods. The problem of an identification of complex processes with the use of neuro-fuzzy systems is solved. The proposed classifier has been successfully applied for building one decision support systems for solving managerial problem.

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The paper is a contribution to the theory of branching processes with discrete time and a general phase space in the sense of [2]. We characterize the class of regular, i.e. in a sense sufficiently random, branching processes (Φk) k∈Z by almost sure properties of their realizations without making any assumptions about stationarity or existence of moments. This enables us to classify the clans of (Φk) into the regular part and the completely non-regular part. It turns out that the completely non-regular branching processes are built up from single-line processes, whereas the regular ones are mixtures of left-tail trivial processes with a Poisson family structure.

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In the presented work the problem of management business-processes with changeable structure is considered and situational based approach to its decision is offered. The approach is based on situational model of management business-process according to which process is represented as a set of situations. The script defining necessary actions is connected with each situation. Management of process is carried out by means of the rules formalizing functional requirements to processes.

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In the given work by authors new approach to the exposure of degree of influencing of medications of vegetable origin in a time of renewal of broken equilibrium of man organism is offered. During realization of the given approach it is suggested to use the mathematical vehicle of.

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2000 Mathematics Subject Classi cation: 49L60, 60J60, 93E20.

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2000 Mathematics Subject Classification: 60J80, 60J10.

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2000 Mathematics Subject Classification: 60G15, 60G60; secondary 31B15, 31B25, 60H15