000			03697nam a22005775i 4500
001			u378194
003			SIRSI
005			20160812084541.0
007			cr nn 008mamaa
008			101119s2011 ne | s |||| 0|eng d
020			_a9789400700086 _9978-94-007-0008-6
040			_cMX-MeUAM
050		4	_aB67
082	0	4	_a501 _223
100	1		_aHaenni, Rolf. _eauthor.
245	1	0	_aProbabilistic Logics and Probabilistic Networks _h[recurso electrónico] / _cby Rolf Haenni, Jan-Willem Romeijn, Gregory Wheeler, Jon Williamson.
264		1	_aDordrecht : _bSpringer Netherlands, _c2011.
300			_aXIII, 155 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSynthese Library, Studies in Epistemology, Logic, Methodology, and Philosophy of Science ; _v350
505	0		_aPreface -- Part I: Probabilistic Logics -- 1. Introduction -- 2. Standard Probabilistic Semantics -- 3. Probabilistic Argumentation -- 4. Evidential Probability -- 5. Statistical Inference -- 6. Bayesian Statistical Inference -- 7. Objective Bayesian Epistemology -- Part II: Probabilistic Networks -- 8. Credal and Bayesian Networks -- 9. Networks for the Standard Semantics -- 10. Networks for Probabilistic Argumentation -- 11. Networks for Evidential Probability -- 12. Networks for Statistical Inference -- 13. Networks for Bayesian Statistical Inference -- 14. Networks for Objective Bayesianism -- 15. Conclusion -- References -- Index.   .
520			_aWhile probabilistic logics in principle might be applied to solve a range of problems, in practice they are rarely applied --- perhaps because they seem disparate, complicated, and computationally intractable. This programmatic book argues that several approaches to probabilistic logic fit into a simple unifying framework in which logically complex evidence is used to associate probability intervals or probabilities with sentences. Specifically, Part I shows that there is a natural way to present a question posed in probabilistic logic, and that various inferential procedures provide semantics for that question, while Part II shows that there is the potential to develop computationally feasible methods to mesh with this framework. The book is intended for researchers in philosophy, logic, computer science and statistics. A familiarity with mathematical concepts and notation is presumed, but no advanced knowledge of logic or probability theory is required.
650		0	_aPhilosophy (General).
650		0	_aGenetic epistemology.
650		0	_aLogic.
650		0	_aScience _xPhilosophy.
650		0	_aComputer science.
650		0	_aDistribution (Probability theory).
650		0	_aMathematical statistics.
650	1	4	_aPhilosophy.
650	2	4	_aPhilosophy of Science.
650	2	4	_aProbability and Statistics in Computer Science.
650	2	4	_aStatistical Theory and Methods.
650	2	4	_aEpistemology.
650	2	4	_aLogic.
650	2	4	_aProbability Theory and Stochastic Processes.
700	1		_aRomeijn, Jan-Willem. _eauthor.
700	1		_aWheeler, Gregory. _eauthor.
700	1		_aWilliamson, Jon. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9789400700079
830		0	_aSynthese Library, Studies in Epistemology, Logic, Methodology, and Philosophy of Science ; _v350
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-94-007-0008-6
596			_a19
942			_cLIBRO_ELEC
999			_c206074 _d206074