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Topic: Bayesian analysis

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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)
	www.astro.cornell.edu /staff/loredo/bayes (2870 words)

	Bayesian spam filtering - Wikipedia, the free encyclopedia
		Bayesian spam filtering is the process of using Bayesian statistical methods to classify documents into categories.
		For instance, Bayesian spam filters will typically have learned a very high spam probability for the words "Viagra" and "refinance", but a very low spam probability for words seen only in legitimate email, such as the names of friends and family members.
		There is recent speculation that even the brain uses Bayesian methods to classify sensory stimuli and decide on behavioural responses (Trends in Neuroscience, 27(12):712-9, 2004) (pdf).
	en.wikipedia.org /wiki/Bayesian_filtering (902 words)

	Bayesian Analysis of U.S. Hurricane Climate
		The approach is Bayesian, combining the reliable records of hurricane activity during the 20th century with the less precise accounts of activity during the 19th century, to produce a best estimate of the posterior distribution on the annual rates.
		The analysis shows that the number of major hurricanes expected to reach the U.S. coast over the next 30 years is 18, while the number of hurricanes expected to hit Florida is 20.
		Bayesian theory provides a framework to define a predictive hurricane climate that uses all the available records, and that can to be used as a benchmark against which future activity is gauged.
	garnet.acns.fsu.edu /~jelsner/HTML/Research/papers/predclim/predclim.html (5486 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)

	Bayesian networks and significance of place (Site not responding. Last check: 2007-10-26)
		The spatial analysis of the Nazi party was conducted at the national scale in order to maximize the number of cases and, therefore, assist in facilitating the robustness of the Bayesian network created later.
		A drawback to the construction of Bayesian networks is the determination of its structure with respect to causality and dependence among the nodes.
		To determine the structure of a Bayesian network, two things are required: some order of the variables that indicates which variables are causes and which are effects, and an assessment of the subset of variables that are conditionally independent of one another.
	www.geovista.psu.edu /publications/others/FHE/fhepaper.htm (9855 words)

	A Plan for Spam
		I thought I was being very clever, but I found that the Bayesian filter did the same thing for me, and moreover discovered of a lot of words I hadn't thought of.
		To beat Bayesian filters, it would not be enough for spammers to make their emails unique or to stop using individual naughty words.
		You could use a Bayesian filter to rate the site just as you would an email, and whatever was found on the site could be included in calculating the probability of the email being a spam.
	www.paulgraham.com /spam.html (5051 words)

	New View of Statistics: Confidence Limits (Site not responding. Last check: 2007-10-26)
		A Bayesian concludes (from the credibility limits of -1.0% to 3.2%) that the drug has anything from a marginal negative effect to a small positive effect.
		(Bayesians give their belief a complete probability distribution, but the principle is the same.) You could--and probably do--base the belief on the results of other studies, but you might just as well meta-analyze these other studies to get your prior "belief".
		Bayesian analysis may be justified where a decision has to be made with limited real data.
	www.sportsci.org /resource/stats/generalize.html (3162 words)

	Weibull-Bayesian Analysis
		In this section, the Bayesian methods are presented for the two-parameter Weibull distribution.
		Bayesian concepts were introduced in the Statistical Background chapter.
		The procedure for obtaining other points of the posterior distribution is similar to the one for obtaining the median values, where instead of 0.5 the percentage of interest is given.
	www.weibull.com /LifeDataWeb/weibull-bayesian_analysis.htm (536 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).
		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 (5784 words)

	Bayesian filter - a definition from Whatis.com
		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.
		Bayesian filters aren't perfect, but because spam characteristically contains certain types of text, such a program can be amazingly effective when it is fine-tuned over a period of time.
	whatis.techtarget.com /definition/0,,sid9_gci957306,00.html (290 words)

	Applied Bayesian Statistics
		By the end of the term, you should be in a position to extend the Bayesian approach in a wide assortment of directions that you can tailor to your own interests.
		Students will be expected to write a fully Bayesian research paper where they apply a technique that they learned in class to a research question that interests them.
		Miracles are not expected—a Bayesian multiple regression would be more than adequate, especially since you may run into computational problems beyond the purview of the class with more sophisticated applications.
	home.uchicago.edu /~grynav/bayes/abs03.htm (954 words)

	Graphical Models (Site not responding. Last check: 2007-10-26)
		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.
		The standard approach in the reconstructibility analysis (RA) community uses the fact that \chi^2(X,Y) \approx I(X,Y) N \ln(4), where N is the number of samples and I(X,Y) is the mutual information (MI) between X and Y. Hence we can use a \chi^2 test to decide whether an increase in the MI score is statistically significant.
	www.cs.berkeley.edu /~murphyk/Bayes/bayes.html (6628 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)

	Bayesian bootstrap for proportional hazards models, Yongdai Kim, Jaeyong Lee
		The binomial form Bayesian bootstrap is the limit of the posterior distribution with a beta process prior as the amount of the prior information vanishes, and thus can be considered as a default nonparametric Bayesian analysis.
		The Poisson form Bayesian bootstrap is equivalent to the Bayesian analysis with Cox's profile likelihood.
		Finally, it is shown that both Bayesian bootstrap posteriors are asymptotically equivalent to the sampling distribution of the maximum likelihood estimator.
	projecteuclid.org /getRecord?id=euclid.aos/1074290331 (563 words)

	Bayesian logic - a definition from Whatis.com
		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.
		The International Society for Bayesian Analysis (ISBA) was founded in 1992 with the purpose of promoting the application of Bayesian methods to problems in diverse industries and government, as well as throughout the Sciences.
		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 (538 words)

	Bayesian Analysis (Site not responding. Last check: 2007-10-26)
		The "Bayesian method" is based on the Bayesian theorem which suggests that the degree to which one believes that a propositioon is true depends on on the a priori belief which one has in the truth of the proposition and in the evidence collected to to investigate the proposition.
		The International Society for Bayesian Analysis (ISBA) was founded in 1992 to promote the development and application of Bayesian statistical theory and methods useful in the solution of theoretical and applied problems in science, industry and government.
		Bayesian statisticians argue that Bayes' s theorem is a formally optimal rule about how to revise opinions in the light of evidence...
	faculty.washington.edu /krumme/450/bayes.html (612 words)

	Nonparametric Bayesian Data Analysis, Peter Müller, Fernando A. Quintana
		Arjas, E. and Heikkinen, J. An algorithm for nonparametric Bayesian estimation of a Poisson intensity.
		In Bayesian Statistics 5 (J. Bernardo, J. Berger, A. Dawid and A. Smith, eds.) 507--511.
		Gray, R. A Bayesian analysis of institutional effects in a multicenter cancer clinical trial.
	projecteuclid.org /Dienst/UI/1.0/Summarize/euclid.ss/1089808275 (2103 words)

	Genome Biology \| Full text \| Bayesian analysis of gene expression levels: statistical quantification of relative mRNA ...
		To that end, we introduce a Bayesian analysis of gene expression level (BAGEL) model for statistical inference of gene expression and demonstrate its utility by re-examining cDNA microarray data on the response of yeast to ethanol shock [11], on transcriptional regulation by SNF2 and SWI1 [12], and on zinc regulation [13].
		For instance, BAGEL analysis of the zinc-regulation data revealed 96 genes whose levels of gene expression in a wild-type genotype and zinc-deficient medium were significantly greater than both zinc-supplemented wild-type and zap1 in zinc-deficient medium.
		A hierarchical Bayesian model [34] has been used to analyze ratio data and provide 95% confidence intervals for the log ratio of gene expression from reference to control.
	genomebiology.com /2002/3/12/research/0071 (8073 words)

	Some Recent References to Bayesian Analysis (Site not responding. Last check: 2007-10-26)
		Berger, “Bayesian Analysis: A Look at Today and Thoughts of Tomorrow,” J. of the American Statistical Association, 95 (Dec., 2000), 1269-1276.
		Bayesian decision frameworks are expressly designed to manage risk.” (p.
		We review the essence of the Bayesian approach and explain why it is particularly useful for marketing problems…We include an extensive discussion of open issues and directions for future research.” (Abstract, p.
	www.bayesian.org /news/azellnerrefs.htm (640 words)

	Bayesian Analysis of Mixture Models with an Unknown Number of Components -- an alternative to reversible jump methods - ...
		Abstract: Richardson and Green (1997) present a method of performing a Bayesian analysis of data from a finite mixture distribution with an unknown number of components.
		140 On Bayesian analysis of mixtures with an unknown number of c..
		2 Bayesian analysis of quantitative trait locus data using rev..
	citeseer.ist.psu.edu /49229.html (780 words)

	Tom Loredo's Bayesian Reprints (Site not responding. Last check: 2007-10-26)
		From Laplace to Supernova SN 1987A: Bayesian Inference in Astrophysics
		The original analyses used Bayesian methods to find credible regions for the two density parameters (due to mass and to a possible cosmological constant), but used an incorrect summary of the evidence for a nonzero cosmological constant (a tail probability rather than a Bayes factor or odds ratio).
		This paper and the next are the second and third in a series applying Bayesian methods to the analysis of the distribution of directions and strengths of gamma-ray bursts.
	astrosun.tn.cornell.edu /staff/loredo/bayes/tjl.html (1544 words)

	[No title]
		Bayesian analysis: a look at today and thoughts of tomorrow.
		Analysis of mixture models using expected posterior priors, with application to classification of gamma ray bursts.
		Bayesian estimation of fuel economy potential due to technology improvements.
	www.isds.duke.edu /~berger/papers.html (1314 words)

	Bayesian Initiative in Health Economics and Outcomes Research (Site not responding. Last check: 2007-10-26)
		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 /default.htm (300 words)

	Bayesian Residual Analysis For Binary Response Regression Models - Albert, Chib (ResearchIndex)
		78 Bayesian analysis of binary and polychotomous response data (context) - Albert, Chib - 1993
		18 A Bayesian approach to outlier detection and residual analys..
		1 Bayesian inference for generalized linear and proportional h..
	citeseer.ist.psu.edu /95618.html (656 words)

	Bayesian Data Analysis
		I last taught this class in Fall 2002 and it will be much the same this time.
		We will be using Bayesian Data Analysis (second edition), by Gelman, Meng, Stern, and Rubin (Chapman and Hall, 2003), as the course text.
		Bayesian binary regression with a probit model using BUGS.
	www.stat.rutgers.edu /~madigan/bayes04 (1011 words)

	A Bayesian Analysis of Metazoan Mitochondrial Genome Arrangements -- Larget et al. 22 (3): 486 -- Molecular Biology and ...
		The analysis in Larget, Simon, and Kadane (2002),
		Bayesian inference of phylogeny and its impact on evolutionary biology.
		Bayesian estimation of the number of inversions in the history of two chromosomes.
	mbe.oxfordjournals.org /cgi/content/full/22/3/486 (5632 words)

	Bookworkz:: Bayesian Methods for Nonlinear Classification and Regression
		Nonlinear Bayesian modelling is a relatively new field, but one that has seen a recent explosion of interest.
		Bayesian methods allow for the incorporation of prior information, allowing the user to make coherent inference.
		Bayesian Methods for Nonlinear Classification and Regression is the first book to bring together, in a consistent statistical framework, the ideas of nonlinear modelling and Bayesian methods.
	www.bookworkz.com /education/statistics/0471490369.html (308 words)

	Amazon.com: Bayesian Data Analysis: Books: Andrew Gelman,John B. Carlin,Hal S. Stern,Donald B. Rubin,A. Gelman (Site not responding. Last check: 2007-10-26)
		Bayesian Methods: An Analysis for Statisticians and Interdisciplinary Researchers (Cambridge Series in Statistical and Probabilistic Mathematics) by Thomas Leonard
		While no Bayesian theory is assumed, it is assumed that the reader has a background in mathematical statistics, probability and continuous multi-variate distributions at a beginning or intermediate graduate level.
		I especially recommend it for researchers who are curious about Bayesian methods but do not see the point of them---Chapter 5, and particularly section 5.5 (an example chosen from educational testing), beautifully addresses this issue.
	www.amazon.com /exec/obidos/tg/detail/-/0412039915?v=glance (2123 words)

	The Math Forum - Math Library - Bayesian Statistics (Site not responding. Last check: 2007-10-26)
		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)

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