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Topic: Bayesian logic

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	Bayesian inference
		Bayesian inference applies the principles of Bayes' theorem and Bayesian probability to derive the rules of logic for propositions in which the degree of belief[?] is not necessarily 1 (certainly true) or 0 (certainly false).
		Bayesian inference has applications in artificial intelligence and expert systems.
		Bayesian inference and fuzzy logic are different approaches to the same tasks, but are not mathematically compatible.
	www.ebroadcast.com.au /lookup/encyclopedia/ba/Bayesian_logic.html (114 words)

	NetLingo.com Dictionary of Internet Terms: Online Dictionary
		A Bayesian filter is a program that uses Bayesian logic (also called Bayesian analysis) to evaluate the header and content of an incoming e-mail message and determine the probability that it constitutes spam.
		Bayesian logic is an extension of the work of the 18th-century English mathematician Thomas Bayes.
		A Bayesian filter works by categorizing e-mail into groups such as "trusted" and "suspect," based on a probability number (ranging from 0 or 0% to 1 or 100%).
	www.netlingo.com /right.cfm?term=Bayesian+filter (238 words)

	Bayesian Logic
		Bayesian Logic is learning – the length of this stage depends on the number of emails a user receives or sends.
		Bayesian Logic is pro-active – the Bayesian Logic has processed enough emails and adjusted to the user’s habits.
		Bayesian Logic is not a miracle cure and does have certain limitations.
	www.secretmaker.com /support/spamfighter/bayesianlogic (733 words)

	Bayesian Epistemology
		Bayesians propose additional standards of synchronic coherence -- standards of probabilistic coherence -- and additional rules of inference -- probabilistic rules of inference -- in both cases, to apply not to beliefs, but degrees of belief (degrees of confidence).
		Subjective Bayesians believe that their position is not objectionably subjective, because of results (e.g., Doob or Gaifman and Snir) proving that even subjects beginning with very different prior probabilities will tend to converge in their final probabilities, given a suitably long series of shared observations.
		On a Bayesian account, the effect of evidence E in confirming (or disconfirming) a hypothesis is solely a function of the increase in probability that accrues to E when it is first determined to be true.
	plato.stanford.edu /entries/epistemology-bayesian (4863 words)

	Bayesian Logic And Filters - Computerworld
		Bayesian logic offers a way to measure things that were previously unmeasurable, allowing us to test hypotheses and predictions and thereby refine our conclusions and decisions.
		The application of Bayesian logic to the spam problem got its start in Paul Graham's 2002 paper "A Plan for Spam" (www.paulgraham.com/spam.html), an approach that was soon adopted by numerous developers.
		Bayesian spam filtering is based on the notion that the presence of certain words will indicate spam, while other words will identify a message as legitimate.
	www.computerworld.com /printthis/2005/0,4814,99476,00.html (786 words)

	Bayesian probability
		Advocates of logical probability would like to codify techniques whereby if two people have the same information relevant to the truth of an uncertain proposition, then they would assign the same probability.
		Bayesian inference is proposed as a model of the scientific method.
		On-line textbook: Information Theory, Inference, and Learning Algorithms, by David MacKay, has many chapters on Bayesian methods, including introductory examples; compelling arguments in favour of Bayesian methods (in the style of Edwin Jaynes); state-of-the-art Monte Carlo methods, message-passing methods, and variational methods; and examples illustrating the intimate connections between Bayesian inference and data compression.
	www.sciencedaily.com /encyclopedia/bayesian_probability (1023 words)

	cause, chance and Bayesian statistics - Bayes theory for conditional and marginal probabilities
		A key feature of Bayesian methods is the notion of using an empirically derived probability distribution for a population parameter.
		The Bayesian approach permits the use of objective data or subjective opinion [2] in specifying a prior distribution [3].
		Bayesian proponents argue, correctly, that the classical methods of statistical inference have built-in subjectivity (through the choice of a sampling plan and the assumption of ‘randomness’ of distributions) and that an advantage of the Bayesian approach is that the subjectivity is made explicit [4].
	www.abelard.org /briefings/bayes.htm (2135 words)

	Wired News: MS Office Helper Not Dead Yet
		Academics agree that Bayesian logic is about to become one of the most pervasive techniques used in computing.
		Bayesian logic is a set of mathematical principles outlined by an obscure but brilliant Presbyterian minister and amateur mathematician, Thomas Bayes, who lived in 18th century England.
		Bayesian logic is being applied to AI, which has been hampered by programs that are inflexible and intolerant of ambiguity.
	www.wired.com /news/technology/0,1282,43065,00.html (920 words)

	Inductive Logic
		Bayesian subjectivists provide a logic that captures this idea, and they attempt to justify this logic by showing that in principle it leads to optimal decisions about which of various risky alternatives should be pursued.
		The notion of logical entailment is interdefinable with it.
		Bayesian inductivists counter that such assessments often play an important role in the sciences, especially when there is insufficient evidence to distinguish among some of the alternative hypotheses.
	plato.stanford.edu /entries/logic-inductive (12775 words)

	Bayesian logic - a Whatis.com definition (Site not responding. Last check: 2007-10-09)
		Named for Thomas Bayes, an English clergyman and mathematician, Bayesian logic is a branch of logic applied to decision making and inferential statistics that deals with probability inference: using the knowledge of prior events to predict future events.
		According to Bayesian logic, the only way to quantify a situation with an uncertain outcome is through determining its probability.
		The modern incarnation of Bayesian logic has evolved beyond Bayes' initial theorem, developed further by the 18th century French theorist Pierre-Simon de Laplace, and 20th and 21st century practitioners such as Edwin Jaynes, Larry Bretthorst, and Tom Loredo.
	whatis.techtarget.com /definition/0,,sid9_gci548993,00.html (541 words)

	UW - Computer Sciences Events
		Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty using probability theory.
		However, Bayesian networks are a probabilistic extension of propositional logic and, hence, inherit some of the limitations of propositional logic, such as the difficulties to represent objects and relations.
		It will be shown that Bayesian logic programs combine the advantages of both definite clause logic and Bayesian networks.
	www.cs.wisc.edu /abstracts/10-23-1_2.html (282 words)

	collision detection: Do tennis players use Bayesian filtering?
		Bayesian logic is a type of analysis that violates old-school logic -- because it incorporates conventional wisdom.
		She's also drawing on a pile of previous experience: Her interactions with other, similar patients, her knowledge of the disease in general, and the life history of that particular patient.
		Most spam filters use Bayesian logic to deduce whether your incoming mail is likely to be spam or not, based on its previous experience of observing not just your mailbox -- but often the mailboxes of the world.
	www.collisiondetection.net /mt/archives/000677.html (410 words)

	ipedia.com: Scientific method Article (Site not responding. Last check: 2007-10-09)
		Bayesian inference has been claimed as a suitable logical basis for discriminating between conflicting hypotheses.
		More than one such logically consistent construct can each paint a usable likeness of the world, but it is pointless to pit them against each other, theory against theory.
		Paul Feyerabend takes these arguments to their limit, arguing that science does not occupy a special place in terms of either its logic or method, and so that any claim to special authority made by scientists cannot be upheld.
	www.ipedia.com /scientific_method_1.html (3543 words)

	Bibliography generated from jc_all.bib
		A Bayesian approach to learning such models from data is taken, with the Metropolis-Hastings algorithm being used to approximately sample from the posterior.
		Bayesian statistical inference is analysed, including an account of its relation to deductive logic and the status of prior distributions.
		Stochastic logic programs (SLPs) and the various distributions they define are presented with a stress on their characterisation in terms of Markov chains.
	www-users.cs.york.ac.uk /~jc/research/pub_chron.html (3852 words)

	Wired News: MS Office Helper Not Dead Yet (Site not responding. Last check: 2007-10-09)
		For example, collaborative filtering, a Bayesian technique used by companies such as Amazon and Tivo to recommend books or TV shows based on the tastes of like-minded users, is also used in Microsoft's Enterprise Server.
		Bayesian techniques are at the heart of the company's forthcoming "dot-net" strategy, which will attempt to centrally manage information to be delivered to any location or device.
		Startups like Autonomy, which provides services to companies that include Intel, Unisys and Perot Systems, are betting the farm that Bayesian logic is the next big thing.
	www.wired.com /news/print/0,1294,43065,00.html (1219 words)

	BAYESIAN LOGIC (Site not responding. Last check: 2007-10-09)
		Bayes'regel (Bayesian inference, Bayesian probability, Bayes'Rule, statistical inference), Bayes'logik (Bayesian inference, Bayesian probability, Bayes'Rule, statistical inference).
		Bayes-regel (Bayesian inference, Bayesian probability, Bayes'Rule, statistical inference), Bayes-logica (Bayesian inference, Bayesian probability, Bayes'Rule, statistical inference).
		Bayes-logiikka (Bayesian inference, Bayesian probability, Bayes'Rule, statistical inference), Bayesin logiikka (Bayesian inference, Bayesian probability, Bayes'Rule, statistical inference).
	www.websters-online-dictionary.org /Ba/Bayesian+logic.html (444 words)

	Bayes
		In the Bayesian view, this must be derived by considering copies of a single experiment and considering the probability that n/N of them have success.
		Bayesian analysis also makes your life infinitely simpler, in the sense that you don't have to run around remembering a zillion different classical-statistical formulae for the case of normal distribution with known mean and unknown variance, unknown mean and known variance, and so on.
		Bayesian ideas are used in any situation where there is uncertainty, whether of a probabilistic nature or otherwise.
	math.ucr.edu /home/baez/bayes.html (8656 words)

	Ongoing research of David Poole
		This is a semantic framework which allows for independent choices made by various agents, and a logic program to give the consequences of the choices.
		This is an expansion of Probabilistic Horn abduction to include a richer logic (including negation as failure), and choices by multiple agents.
		Bayesian networks, influence diagrams, where we use rules to specify the conditional probability tables, the value function, and what information will be available when the agent must make a decision.
	www.cs.ubc.ca /spider/poole/iclstory.html (1301 words)

	Kevin B. Korb (Site not responding. Last check: 2007-10-09)
		I give a Bayesian analysis of natural language arguments and some of the traditional fallacies.
		The goal of such a theory is to apply Bayesian principles to issues in scientific and statistical methodology, so as to improve our understanding and practice in scientific research and in those areas of artificial intelligence which emulate scientific research, such as machine learning.
		Bayesian AI We are developing Bayesian networks, and techniques for using Bayesian nets, in a variety of areas, including game playing, planning, financial applications, etc. We are available for consulting projects in industry and have developed a formal tutorial course
	www.csse.monash.edu.au /~korb (2348 words)

	Dealing with comment spam using Bayesian logic \| drupal.org
		Instead, I read up on using Bayesian logic, and ultimately decided it would be best to write a simple Bayesian filter in PHP.
		In particular, while the underlying logic is believed to be fully functional it has not been optimized for best performance, the administrative interface used to control the module is still rough, and the module doesn't actually take any actions when it detects spam.
		I'm happy to report that on the 37'th spam comment posted to KernelTrap the Bayesian logic caught its first spam comment in the wild.
	drupal.org /node/11129 (2959 words)

	[No title] (Site not responding. Last check: 2007-10-09)
		Inspired by game and decision theory representations, Bayesian networks, Markov decision processes, influence diagrams, logic programming, abductive reasoning, and work in dynamical systems, the independent choice logic (ICL) is a semantic framework that allows for independent choices (made by various agents including nature) and a logic program that gives the consequence of choices.
		He is the author of over 60 refereed journal and conference papers and is a coauthor of a recent AI textbook published by Oxford University Press entitled "Computational Intelligence: A Logical Approach" and co-editor of the "Proceedings of the Fourteenth Conference on Uncertainty in AI".
		His main research interests are logic for decision making, reasoning under uncertainty, logic programming, nonmonotonic reasoning, reasoning about action, diagnosis, and reactive agents.
	cs.anu.edu.au /lib/seminars/seminars98/dept980408 (339 words)

	Bayesian Inductive Logic Programming - Muggleton (ResearchIndex) (Site not responding. Last check: 2007-10-09)
		Abstract: Inductive Logic Programming (ILP) involves the construction of first-order definite clause theories from examples and background knowledge.
		ILP systems have successful applications in the learning of structure-activity rules for drug design, semantic grammars rules, finite element mesh design rules and rules for prediction of protein...
		84 Bounds on the sample complexity of bayesian learning using i..
	citeseer.ist.psu.edu /muggleton94bayesian.html (806 words)

	LiEP CIS-2000-034: Bayesian Logic Programs. (Site not responding. Last check: 2007-10-09)
		They are a probabilistic extension of propositional logic and, hence, inherit some of the limitations of propositional logic, such as the difficulties to represent objects and relations.
		The main contribution of this extended abstract is to introduce a new approach, called Bayesian logic programs, to overcome the limitations.
		Furthermore, Bayesian logic programs generalize both Bayesian networks as well as logic programs, many ideas developed in both areas can be adapted.
	www.ida.liu.se /ext/epa/cis/2000/034/cover.html (158 words)

	Interpreting Bayesian Logic Programs - Kersting, De Raedt, Kramer (ResearchIndex) (Site not responding. Last check: 2007-10-09)
		We introduce the formalism of Bayesian logic programs, which is basically a simpli - cation and reformulation of Ngo and Haddawys probabilistic logic programs.
		However, Bayesian logic programs are suciently powerful to represent essentially the same knowledge in a more elegant manner.
		The elegance is illustrated by the fact that they can represent both Bayesian nets and de nite clause programs (as in \pure"...
	citeseer.lcs.mit.edu /kersting00interpreting.html (375 words)

	USATODAY.com - Bayesian spam filters use math that works like magic (Site not responding. Last check: 2007-10-09)
		First, a Bayesian filter has to be trained; you need to tell it these are legit messages and these are spam.
		After going through this training process, the Bayesian software ends up with a list of words and their spammish probabilities (often called a "corpus").
		Bayesian filters work because they aren't dependent on things that might change: the way words are spelled, or where a message comes from.
	www.usatoday.com /tech/columnist/andrewkantor/2004-09-17-kantor_x.htm (1288 words)

	Bayesian inference - InfoSearchPoint.com (Site not responding. Last check: 2007-10-09)
		Bayesian inference is statistical inference in which probabilities are interpreted not as frequencies or proportions or the like, but rather as degrees of belief.
		In other words, it attempts to reduce statistical inference to Bayesian probability.
		On-line textbook: Information Theory, Inference, and Learning Algorithms, by David MacKay, has many chapters on Bayesian methods, including introductory examples; compelling arguments in favour of Bayesian methods; state-of-the-art Monte Carlo methods, message-passing methods, and variational methods; and examples illustrating the intimate connections between Bayesian inference and data compression.
	www.infosearchpoint.com /display/Bayesian_logic (172 words)

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