000 03195nam a22005175i 4500
001 978-1-4419-5548-7
003 DE-He213
005 20140220084507.0
007 cr nn 008mamaa
008 100528s2010 xxu| s |||| 0|eng d
020 _a9781441955487
_9978-1-4419-5548-7
024 7 _a10.1007/978-1-4419-5548-7
_2doi
050 4 _aQA273.A1-274.9
050 4 _aQA274-274.9
072 7 _aPBT
_2bicssc
072 7 _aPBWL
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.2
_223
100 1 _aIvanenko, V. I.
_eauthor.
245 1 0 _aDecision Systems and Nonstochastic Randomness
_h[electronic resource] /
_cby V. I. Ivanenko.
250 _a1.
264 1 _aNew York, NY :
_bSpringer New York,
_c2010.
300 _aXII, 272p. 6 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aDecision Systems -- Indifferent Uncertainty -- Nonstochastic Randomness -- General Decision Problems -- Experiment in Decision Problems -- Informativity of Experiment in Bayesian Decision Problems -- Reducibility of Experiments in Multistep Decision Problems -- Concluding Remarks.
520 _a“Decision Systems and Nonstochastic Randomness” presents the first mathematical formalization of the statistical regularities of non-stochastic randomness and demonstrates how these regularities extend the standard probability-based model of decision making under uncertainty, allowing for the description of uncertain mass events that do not fit standard stochastic models. Each self-contained chapter of this neatly-structured monograph includes a detailed introduction and summary of its contents. The included results are presented not only with rigorous proofs but also through numerous intuitive examples. An appendix is provided which includes classic results from the theory of functions and measured sets as well as decision theory, offering an overview of the necessary prerequisites. The formalism of statistical regularities developed in this book will have a significant influence on decision theory and information theory as well as numerous other disciplines. Because of these far-reaching implications, this book may be a useful resource for statisticians, mathematicians, engineers, economists and other utilizing nonstochastic modeling and decision theory.
650 0 _aMathematics.
650 0 _aDistribution (Probability theory).
650 0 _aMathematical statistics.
650 0 _aEconomics
_xStatistics.
650 1 4 _aMathematics.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aGame Theory, Economics, Social and Behav. Sciences.
650 2 4 _aOperations Research/Decision Theory.
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441955470
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4419-5548-7
912 _aZDB-2-SMA
999 _c110480
_d110480