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Topic: Bayesian statistics

In the News (Sat 11 Oct 08)

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	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
		Bayesian probability is an interpretation of probability suggested by Bayesian theory, which holds that the concept of probability can be defined as the degree to which a person believes a proposition.
		Bayesian probability contrasts with frequency probability, in which probability is derived from observed frequencies in defined distributions or proportions in populations.
		Bayesians typically reply that differences due to alternative models of the data generating process are equally subjective, and that such model choices are also (ideally) chosen prior to analysis of the data, by the analyst.
	en.wikipedia.org /wiki/Bayesian_probability (2130 words)

	AMS Research (Bayesian Statistics)
		Statistics is the study of uncertainty -- how to measure it, and what to do about it.
		The Bayesian approach, which quantifies uncertainty through the use of probability distributions for all unknown quantities, is more general, but for centuries its progress was held back by a formidable technical challenge: the accurate numerical evaluation of high-dimensional integrals defined by these probability distributions.
		Around 1990 the statistics profession learned about a class of methods -- invented by physicists in the early 1950s -- for solving this problem by simulating from the relevant probability distributions, and noticed that computers had finally become fast enough for this approach to become practical.
	www.cse.ucsc.edu /~draper/bayes.html (279 words)

	Bayesian inference - Wikipedia, the free encyclopedia
		Bayesian inference is statistical inference in which evidence or observations are used to update or to newly infer the probability that a hypothesis may be true.
		Bayesian inference uses aspects of the scientific method, which involves collecting evidence that is meant to be consistent or inconsistent with a given hypothesis.
		Bayesian inference can be used in a court setting by an individual juror to coherently accumulate the evidence for and against the guilt of the defendant, and to see whether, in totality, it meets their personal threshold for 'beyond a reasonable doubt'.
	en.wikipedia.org /wiki/Bayesian_statistics (3659 words)

	cause, chance and Bayesian statistics - Bayes theory for conditional and marginal probabilities
		Cause, chance and Bayesian statistics is one in a series of documents showing how to apply empiric reasoning to social and psychological problems.
		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)

	Bayesian Statistics
		Bayesian methods are an important aid to archaeological data interpretation because we very often have relatively little data but considerable, informative prior information which is complex and hard to interpret heuristically.
		Bayesian methods have been shown to be of particular interest in the interpretation of particularly noisy field survey results, in particular soil phosphate data.
		By the mid-1990s Bayesian methods for radiocarbon calibration had been appearing in both the statistics and archaeology literature for some time and we had developed quite a large number of practical software tools for helping archaeologists interpret their data.
	www.shef.ac.uk /pas/research/clusters/bayesian/archaeology.html (2306 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 unifies Bayesian epistemology is a conviction that conditionalizing (perhaps of a generalized sort) is rationally required in some important contexts — that is, that some sort of conditionalization principle is an important principle governing rational changes in degrees of belief.
		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 (5773 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)

	Overview to Bayesian Statistics (Site not responding. Last check: 2007-09-17)
		The Bayesian philosophy is that if you are the one who needs to make a decision, then you should bring all of your resources to bear on that decision, rather than rely solely on the outcome of an experiment.
		This is not largely due to a change in philosophy by those using statistics, but rather due to the rise in computing power that has made Bayesian statistics feasible.
		An advantage of Bayesian statistics is that rather complex models can be analyzed without the need to build inferential statistics up from scratch with small changes to the model.
	www-math.cudenver.edu /~bbailey/5396/overview.html (393 words)

	PrismEmail - Detailed Explanation of Bayesian Filtering
		Bayesian filtering is a method of filtering spam using statistics.
		The Bayesian approach is actually quite simple: It calculates the probability that a given message is spam or not based on the contents of that message and based on the contents of past messages and past spam that you have received.
		While we won't go into the mathematics behind it, Bayesian statistics tells us that if the word click appears in 35 of 1000 good emails and in 750 of 1000 spam emails, then the presence of the word "click" means that the given message has a 95.54% chance of being spam.
	www.prismemail.com /bayesianfilter.php (1801 words)

	Amazon.fr : Bayesian Statistics And Marketing: Livres en anglais: Peter E. Rossi,Greg M. Allenby,Rob McCulloch (Site not responding. Last check: 2007-09-17)
		Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources.
		Bayesian Statistics and Marketing describes the basic advantages of the Bayesian approach, detailing the nature of the computational revolution.
		Bayesian Statistics and Marketing provides a platform for researchers in marketing to analyse their data with state-of-the-art methods and develop new models of consumer behaviour.
	www.amazon.fr /Bayesian-Statistics-Marketing-Peter-Rossi/dp/0470863676 (513 words)

	Bayesian Initiative in Health Economics and Outcomes Research (Site not responding. Last check: 2007-09-17)
		This Primer is for health economists, outcomes research practitioners and biostatisticians who want to understand the basics of Bayesian statistics and how Bayesian methods may be applied in the economic evaluation of health care technologies.
		Participants will learn how a Bayesian approach is different and why it is useful for their work and what tools are available to them.
		The objective of the Bayesian Initiative in Health Economics and Outcomes Research ("The Bayesian Initiative") is to explore the extent to which formal Bayesian statistical analysis can and should be incorporated into the field of health economics and outcomes research for the purpose of assisting rational health care decision-making.
	www.bayesian-initiative.com (300 words)

	Bayesian Statistics
		The premise of Bayesian statistics (within the context of life data analysis) is to incorporate prior knowledge, along with a given set of current observations, in order to make statistical inferences.
		This section is intended to give a quick and elementary overview of Bayesian methods, focused primarily on the material necessary for understanding the Bayesian analysis methods available in Weibull++.
		For example, in Bayesian analysis the parameters of the distribution to be "fitted" are the random variables.
	www.weibull.com /LifeDataWeb/bayesian_statistics.htm (1050 words)

	Amazon.com: The Bayesian Choice (Springer Texts in Statistics): Books: Christian P. Robert (Site not responding. Last check: 2007-09-17)
		It is a textbook that presents an introduction to Bayesian statistics and decision theory for graduate level course ….
		Bayesian hierarchical models are now being used in the development of clinical trials particularly in the medical device industry.
		This is an advanced graduate text in Bayesian statistics and has a wealth of references to the literature.
	www.amazon.com /Bayesian-Choice-Springer-Texts-Statistics/dp/0387952314 (1535 words)

	Presentation: An Introduction to the Draft Document: The Use of Bayesian Statistics in the Medical Device Clinical ...
		Draft provides guidance on statistical aspects of the design and analysis of clinical trials for medical devices that use Bayesian statistical methods.
		This documents reflects FDA's careful review of what we believe are the issues related to the use of Bayesian statistic in medical device clinical trials and what we believe would be the least burdensome way of addressing them.
		Statistical theory and approach to data analysis that provides a coherent method from learning from evidence as it accumulates.
	www.fda.gov /cdrh/meetings/072706-bayesian/presentation1.html (714 words)

	Bayesian Statistics
		Bayesian statistical methods are used extensively in the two important parts of bioinformatics:
		The major difference between Bayesian Statistics and other statistical methods is that Bayesian statistical methods use conditional probabilities.
		Excellent tutorials by Dr. Lawrence as well as software related to Bayesian Statistics are available from his laboratory web pages: ftp://ftp.wadsworth.org/pub/bioinfo/software and http://www.wadsworth.org/resnres/bioinfo/ (opens new window).
	www.bioinfo.rpi.edu /applications_new/bayesian (119 words)

	Bayes' Theorem (Stanford Encyclopedia of Philosophy)
		Subjectivists, who maintain that rational belief is governed by the laws of probability, lean heavily on conditional probabilities in their theories of evidence and their models of empirical learning.
		The key to the argument lies in marrying the "minimal" version of Bayesian expressed in the (2.1e) to a very modest "proportioning" requirement for belief revision rules.
		The basic Bayesian insight embodied in the weak likelihood principle (2.1e) entails that simple and Jeffrey conditioning on E are the only rational ways to revise beliefs in response to a learning experience whose sole immediate effect is to alter E's probability.
	plato.stanford.edu /entries/bayes-theorem (7479 words)

	Bayesian Statistics - Research - Newcastle University
		Bayesian point null hypothesis testing via the posterior likelihood ratio.
		Bayesian inference for stochastic kinetic models using a diffusion approximation.
		Bayesian regression and classification using mixtures of Gaussian process.
	www.ncl.ac.uk /math/research/statistics/bayes (369 words)

	Bayesian Initiatives - News Room (Site not responding. Last check: 2007-09-17)
		During this conference, participants from academia, industry, and government agencies had a chance to discuss and debate the Bayesian approach versus the classical statistical approach and explore the value of the Bayesian approach to health economics and outcomes research.
		A request for proposal entitled “Building a Reference Case for Bayesian Applications to Health Economics and Outcomes Research” was issued in Spring 2000 to stimulate more research involving the Bayesian approach.
		The Bayesian Initiative has sponsored several educational workshops, short courses, and presentations at national or international meetings to introduce the Bayesian approach to health economists and outcomes researchers.
	www.bayesian-initiative.com /Bay_News.htm (411 words)

	The Math Forum - Math Library - Bayesian Statistics
		Introductory material on Bayesian networks and the probabilities involved, with a Bayesian Networks Bibliography of resources for Computational Complexity; Fielded Systems; General References, Tutorials, and Surveys; Knowledge Engineering and Maintenance; Knowledge Representation and Structure; Learning; Reasoning Algorithms; Research Applications; Tools/Software for Bayesian Networks; and Abductive Reasoning.
		Promotes the development of Bayesian statistical theory and its application to problems in science, industry and government.
		The Math Forum is a research and educational enterprise of the Drexel School of Education.
	mathforum.org /library/topics/baysean_stat (451 words)

	PrismEmail - Bayesian Filtering FAQ - How Bayesian Works
		When Bayesian statistics is enabled, the system will maintain a statistical file that counts how many times each word has appeared in your real email and in spam.
		Once the Bayesian filter is operating you may still want to leave the other filters enabled since we may be able to help you filter out known spam websites that may not be part of your Bayesian statistics.
		With statistics for 1000 good messsages and 4000 spam messages we have seen the Bayesian filter catch 99.8% of the spam received.
	www.prismemail.com /bayesianfaq.php (752 words)

	Graphical Models
		The simplest conditional independence relationship encoded in a Bayesian network can be stated as follows: a node is independent of its ancestors given its parents, where the ancestor/parent relationship is with respect to some fixed topological ordering of the nodes.
		Note that "temporal Bayesian network" would be a better name than "dynamic Bayesian network", since it is assumed that the model structure does not change, but the term DBN has become entrenched.
		In principle, it is straightforward to use graphical models to do Bayesian learning: the parameters, being random variables, become nodes as well, and the goal is the standard inference problem of computing posterior distributions on the (parameter) nodes.
	www.cs.ubc.ca /~murphyk/Bayes/bayes.html (6628 words)

	Bayesian Statistics
		Bayesian Inference is a paradigm in Statistics that attempts to utilize all available information in decision-making.
		These methods allow the use of models of complex physical phenomena that were previously too difficult to estimate.
		Bayesian methods offer a means of more fully understanding issues that are central to many practical problems by allowing researchers to build integrated models of behavior that can be estimated with limited amounts of data.
	www.stat.ncsu.edu /bswg (205 words)

	Wiley::Introduction to Bayesian Statistics
		There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods.
		In Bayesian statistics the rules of probability are used to make inferences about the parameter.
		Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used.
	www.wiley.com /WileyCDA/WileyTitle/productCd-0471270202.html (219 words)

	No. 1876: Bayesian Statistics
		If the likelihood of your wife's picking a white dog is fifteen percent, and her friend's doing so is ninety percent, the odds that her friend chose it turn out to be eighty-five percent.
		The Times article describes a Bayesian thought process as what we use when "uncertainty becomes great enough to give past experience an edge over current observation." Of course that can be very dicey.
		By now, the obvious usefulness of Bayesian statistics has triumphed over the equally obvious dangers that go with its use.
	www.uh.edu /engines/epi1876.htm (863 words)

	Bayesian statistics
		The Bayesian way of estimating the parameters of a given model focuses around the Bayes theorem.
		The key idea in Bayesian statistics is to work with full distributions of parameters instead of single values.
		In calculations that require a value for a certain parameter, instead of choosing a single ``best'' value, one must use all the values and weight the results according to the posterior probabilities of the parameter values.
	www.cis.hut.fi /ahonkela/dippa/node20.html (293 words)

	Wiley::Bayesian Statistics and Marketing
		Bayesian Analysis in Statistics and Econometrics: Essays in Honor of Arnold Zellner
		Presents a unified treatment of Bayesian methods in marketing, with common notation and algorithms for estimating the models.
		Includes case studies drawn from the authors’ recent research to illustrate how Bayesian methods can be extended to apply to many important marketing problems.
	www.wiley.com /WileyCDA/WileyTitle/productCd-0470863676.html (422 words)

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