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Topic: Bayesian

	BIPS: Bayesian Inference for the Physical Sciences
		From Laplace to SN 1987A: Bayesian inference in astrophysics (Loredo 1990)
		From Laplace to Supernova SN 1987A: Bayesian Inference in Astrophysics (1990)
		The Promise of Bayesian Inference for Astrophysics (1992)
	astrosun2.astro.cornell.edu /staff/loredo/bayes (2872 words)

	Bayesian probability - Wikipedia, the free encyclopedia
		Bayesianism is the philosophical tenet that the mathematical theory of probability applies to the degree of plausibility of a statement.
		Whereas a frequentist and a Bayesian might both assign a 1/2 probability to the event of getting a head when a coin is tossed, only a Bayesian might assign 1/1000 probability to a personal belief in the proposition that there was life on Mars a billion years ago.
		The Bayesian approach is in contrast to the concept of frequency probability where probability is held to be derived from observed or imagined frequency distributions or proportions of populations.
	en.wikipedia.org /wiki/Bayesian_probability (1657 words)

	An Introduction to Bayesian Networks and their Contemporary Applications
		Bayesian Networks are becoming an increasingly important area for research and application in the entire field of Artificial Intelligence.
		Bayesian networks are useful for both inferential exploration of previously undetermined relationships among variables as well as descriptions of these relationships upon discovery.
		Bayesian inference is useful because it allows the inference system to construct its own potential systems of meaning upon the data.
	www.niedermayer.ca /papers/bayesian (3803 words)

	[No title]
		Bayesians claim that their methods could make clinical trials of drugs faster and fairer, and computers easier to use.
		Bayesian methods offer the possibility of modifying a trial while it is being conducted, something that is impossible with traditional statistics.
		Bayesian methods can also be used to decide between several competing hypotheses, by seeing which is most consistent with the available data.
	www.chebucto.ns.ca /Science/AIMET/archive/bayes (1221 words)

	Bayesianism (Site not responding. Last check: )
		For this reason, Bayesians prefer to regard them only as certain idealizations or heuristic conventions rather than empirical hypotheses, especially that they are to be norms of rationality rather than a description or generalization of experimental data.
		Bayesianism is most often identified as a certain theory of confirmation, and at the same time as the most ambitious attempt to provide a uniform and general explanation of scientific knowledge.
		Moderate Bayesians hold that it is not a complete philosophy of science and epistemology but only a certain idealizing model, but a model that allows a clear and precise explanation and solution for many problems that result from a simplified treatment of beliefs as exclusively true or false.
	www.kul.lublin.pl /efk/angielski/hasla/b/bayesianism.html (1671 words)

	Re: Bayesian & Frequentist Probability Theory
		The basic difference between Bayesians and frequentists is this: Bayesians condition on the data actually observed, and consider the probability distribution on the hypotheses; they believe it reasonable to put probability distributions on hypotheses and they behave accordingly.
		Bayesian probability is not necessarily subjective; there is a school of objective Bayesianism, typified by people like Harold Jeffreys, the British astronomer/geophysicist/statistician, and Ed Jaynes, the physicist at Washington University in St. Louis.
		Furthermore, Bayesian inference is squarely linked to the likelihood function, which even a frequentist admits has a probability interpretation (the likelihood function is proportional to the sampling distribution, which frequentists use as the basis of frequentist inference).
	www.lns.cornell.edu /spr/2002-03/msg0040638.html (1949 words)

	20th WCP: Coherence and Epistemic Rationality
		Degrees of confidence have mainly been discussed by Bayesians as part of a general theory of rational belief and decision.
		Bayesians are frequently criticized, and often dismissed, for making the unrealistic assumption that agents consider and attach a probability to every proposition in advance of any empirical inquiry.
		However, none of the central Bayesian principles such as choosing so as to maximize expected utility, or updating beliefs by conditionalization requires the unrealistic assumption that an agent be fully opinionated.
	www.bu.edu /wcp/Papers/TKno/TKnoVine.htm (3166 words)

	Degrees of Freedom in the Social World (Site not responding. Last check: )
		If Searle is right, then Bayesian accounts of decision, in all their many and bewildering varieties, are misconceived, as also are the accounts of social behavior which rest on them, even as the latter aspire to the rigor of science.
		For these Bayesian accounts presuppose both that agents aim exclusively at individual goals, by maximizing expected individual utilities (partly through modeling the deliberations of goal-directed others precisely as they model the undirected forces of nature),whilst at the same time presupposing that it is an imperative of reason to proceed in this fashion.
		It shall be my thesis that, while the Bayesian approach expects the deliberator to be a consummate logician, as a consequence of being consummately rational, and in that regard exaggerates the human endowment, it at the same time understates her other resources.
	wings.buffalo.edu /philosophy/FARBER/thalos.html (5446 words)

	[No title]
		There is even a Bayes songbook—though, since Bayesians are an academic bunch, it is available only in the obscure file formats that are used for scientific papers.
		Previous convictions The essence of the Bayesian approach is to provide a mathematical rule explaining how you should change your existing beliefs in the light of new evidence.
		In a paper to be published shortly in the Journal of Statistical Planning and Inference, he sets out to demystify the Bayesian approach, and explains how to apply it after the event to existing data.
	www.columbia.edu /~dj114/bayes02.doc (1397 words)

	Bayesian Epistemology (Stanford Encyclopedia of Philosophy)
		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).
		What unites all of the Objective Bayesians is their conviction that in many circumstances, symmetry considerations uniquely determine the relevant prior probabilities and that even when they don't uniquely determine the relevant prior probabilities, they often so constrain the range of rationally admissible prior probabilities, as to assure convergence on the relevant posterior probabilities.
		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 (6936 words)

	Math Forum - Ask Dr. Math (Site not responding. Last check: )
		The difference between Bayesian statistics and regular (Frequentist) statistics is essentially a different interpretation of what probability signifies, and thus a different way to make an inference about a population given that we have a sample of that population.
		The Bayesian DOES have a clue how close this particular realization of his estimator to the speed of light, because, unlike the Frequentist, she can make a probability statement about this realization.
		In Bayesian statistical inference, we first make a guess on what the probability distribution of the parameter in question is. This is called a prior distribution.
	mathforum.org /library/drmath/view/52221.html (942 words)

	An Intuitive Explanation of Bayesian Reasoning
		Bayesian reasoning is apparently one of those things which, like quantum mechanics or the Wason Selection Test, is inherently difficult for humans to grasp with our built-in mental faculties.
		The Bayesian Conspiracy is a multinational, interdisciplinary, and shadowy group of scientists that controls publication, grants, tenure, and the illicit traffic in grad students.
		The Bayesian revolutionaries hold that when you perform an experiment and get evidence that "confirms" or "disconfirms" your theory, this confirmation and disconfirmation is governed by the Bayesian rules.
	yudkowsky.net /bayes/bayes.html (13212 words)

	No Free Lunches
		Bayesian conditionalization is a learning rule that falls within the scope the no-free-lunch theorems.
		So it is puzzling to see Bayesians seek a priori justifications for the rule in terms of coherence if coherent and incoherent rules have the same a priori probability of predictive success.
		It may be that coherent rules are more successful, in fact, but their coherence does not automatically explain that success.
	philosophy.wisc.edu /forster/papers/no-free-lunch.htm (682 words)

	Bayesians [B-Course] (Site not responding. Last check: )
		Bayesian uses probability to asses her uncertainty about the true existence and nature of dependencies.
		Bayesian is very willing to say that the probability that A depends on B is 0.9.
		Looking the data by using dependency model can reveal interesting things about your world, and you can even attach probabilities to different background assumptions, but all in all, Bayesians are rather reluctant to claim that their results are the one and only objective truth.
	b-course.cs.helsinki.fi /bayesians.html (340 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.
	www.math.ucr.edu /home/baez/bayes.html (8656 words)

	Deinonychus antirrhopus: 100,000 Dead Iraqi's and Statistics (Site not responding. Last check: )
		Now in Bayesian analysis we can construct intervals much like the confidence interval, but since the parameters that are being estimated are themselves random and have distribtuions, the result intervals do have meaningful probabilistic interpretations.
		That is the 95% interval in Bayesian analsysis means that the paremeter is contained in the interval with a probability of 95% (or more correctly a probability of 0.95).
		Bayesians don't generally talk about confidence since their methodology allows them to make meaningful probability statements about the parameter of interest.
	www.steveverdon.com /archives/statistics/002156.html (5835 words)

	Statistics notes: Bayesians and frequentists -- Bland and Altman 317 (7166): 1151 -- BMJ
		Bayesian methods are based on the idea that unknown quantities, such as population means and proportions, have probability
		Most statisticians have become Bayesians or frequentists as a result of their choice of university.
		Bayesians and frequentists existed until it was too late and the
	bmj.bmjjournals.com /cgi/content/full/317/7166/1151 (800 words)

	[No title] (Site not responding. Last check: )
		This example shows that frequentist methods are dependent on experimental design, whereas Bayesian methods adhere to a general principle, the Likelihood Principle, which says that all interpretations of an experiment must rely on the likelihood of observed data.
		The fundamental dividing line seems to be that frequentists do not accept probability as dependent on information, whereas Bayesians have to attack the problem of translating information to priors, which is difficult and delicate.
		The frequentists observe a non-robustness of Bayesian conclusions caused by reliance on priors which seem to be essentially ad hoc.
	www.nada.kth.se /~stefan/sem1008.html (209 words)

	October 7, 1996, UCLA Statistics Seminar (Site not responding. Last check: )
		Kiefer [J. of the American Statistical Association 72 (1977), 789-827] sought to overcome these problems by developing a "conditional frequentist" theory, in which the error probabilities are allowed to depend on the data.
		That the Bayesian answers can be given a valid frequentist interpretation is quite surprising.
		Even more surprising is that the Bayesian answers can often be shown to be the optimal frequentist answer, better than the Newman-Pearson fixed error probabilities.
	www.stat.ucla.edu /seminars/seminars/fall96/abstracts/oct7.php (280 words)

	8.1.10. How can Bayesian methodology be used for reliability evaluation?
		Parametric Bayesian prior models are chosen because of their flexibility and mathematical convenience.
		Here we compare the classical paradigm versus the Bayesian paradigm when system reliability follows the HPP or exponential model (i.e., the flat portion of the Bathtub Curve).
		While the primary motivation to use Bayesian reliability methods is typically a desire to save on test time and materials cost, there are other factors that should also be taken into account.
	www.itl.nist.gov /div898/handbook/apr/section1/apr1a.htm (1039 words)

	Bayesian Fun (Site not responding. Last check: )
		A Bayesian is one who, vaguely expecting a horse, and catching a glimpse of a donkey, strongly believes he has seen a mule.
		A Bayesian and a Frequentist were to be executed.
		So when the baby actually arrived, the actual physical evidence that the baby was a girl was not strong enough to overcome her prior (ahem) belief that the baby would be a boy, and so Joshua and Anne named their new baby daughter Thomas and raised her to be the son they had always wanted.
	www.isye.gatech.edu /~brani/isyebayes/jokes.html (641 words)

	[No title]
		I believe the Bayesians are corrupt, or bankrupt, or in any case, up to no good; I believe that we accept statements as firmly as I believe anything.
		It may be too painful for Bayesians and others to acknowledge, but it is what we already do, at least when we are rational.
		A Bayesian knowledge engineer will condition on statements that are contingent, hence, not knowable with certainty, hence accepted.
	www.cs.wustl.edu /~loui/kybwagens.text (797 words)

	AASG Meeting Notes, Dr Jon Kvanvig on Faith (Site not responding. Last check: )
		Bayesians will tell you that probability and belief are both subjective.
		Bayesians say there is no such things as belief, there are only degrees of belief.
		For instance, a Bayesian might offer you the choice to bet $100 on two things and based on your answer they determine the degree of belief you have in the thing that you bet on.
	freethought.tamu.edu /~aasg/aleph/notes/kvanvig.html (654 words)

	Acquiring Statistics \| Jimmie Savage (Site not responding. Last check: )
		That is how Bayesians say "one-sixth." Putting anything into other terms excites mathematicians, and we are here on the verge of a total restatement of expectation theory in Bayesian terms.
		Bayesians reject intrinsic probabilities (the idea that the probability lies in the object).
		But for Bayesians, the initial probalities can't be taken from the object, they must also lie with the subjective person, who has, by definition, in advance of experience, no clue what they are.
	www.umass.edu /wsp/statistics/tales/savage.html (1755 words)

	bays
		Bayesian research activities are now very active in Brazilian universities with strong interaction with universities abroad.
		Bayesian statistics in Chile is at its early stages, but starting to grow and develop.
		The same year we organised one of Zellner's meetings on Bayesian statistics and econometrics, which was well attended by many statisticians from the Americas and left some people pondering the good properties of the añejo distribution for a while.
	www.est.ufmg.br /~loschi/bays.html (4078 words)

	[No title]
		Apparently nothing that Bayesians themselves have said: the assumption in question is almost never explicitly defended in the literature.
		Bayesians argue that E does confirm H—but only to a minute degree, given that there are overwhelmingly more nonfl objects than there are ravens.
		Aronson, Jerold R. (1989), “The Bayesians and the raven paradox”, Noûs 23: 221-240.
	www.public.iastate.edu /~vranas/Homesite/papers/hempelacunaweb.doc (3987 words)

	ISBA
		The International Society for Bayesian Analysis (ISBA) was founded in 1992 to promote the development and application of Bayesian analysis useful in the solution of theoretical and applied problems in science, industry and government.
		By sponsoring and organizing meetings, publishing the electronic journal of Bayesian statistics Bayesian Analysis, and other activities ISBA provides a focal point for those interested in Bayesian analysis and its applications.
		His solution, known as Bayes' theorem, underlies, and gave its name to, the modern Bayesian approach to the analysis of all kinds of data.
	www.bayesian.org (451 words)

	Better Bayesian Filtering
		Spam filtering is a subset of text classification, which is a well established field, but the first papers about Bayesian spam filtering per se seem to have been two given at the same conference in 1998, one by Pantel and Lin [2], and another by a group from Microsoft Research [3].
		So their numbers may not even be an accurate measure of the performance of their algorithm, let alone of Bayesian spam filtering in general.
		If you were doing Bayesian filtering in a situation where the ratio of spam to nonspam was consistently very high or (especially) very low, you could probably improve filter performance by incorporating prior probabilities.
	www.paulgraham.com /better.html (4059 words)

	Re: Bayesian & Frequentist Probability Theory
		In article , Bill Jefferys writes: >In article , Charles Francis > wrote: >The basic difference between Bayesians and frequentists is this: >Bayesians condition on the data actually observed, and consider the >probability distribution on the hypotheses; they believe it reasonable >to put probability distributions on hypotheses and they behave >accordingly.
		It is just a matter of whether you use Bayes theorem to invert the probability, or whether you ask an inverted question which, as you say, most non statisticians promptly misinterpret.
		A frequentist uses probability theory to predict objective frequency distributions, whereas a Bayesian also uses probability theory to encapsulate subjective assessment of likelihood.
	www.lns.cornell.edu /spr/2002-04/msg0040863.html (846 words)

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