Bayes and empirical Bayes (EB) methods structure combining information from similar components of information?
100712
It is developed from the definition of conditional probability. To find the conditional probability of B given A. In stead of A given B, what is normally the case.
209465
you are looking for NEURAL NETWORKS. Try those two books:\nbayesian artifical intelligence, by Kevin Korb & Ann Nicholson (ISBN 1-58488-387-1) and\nNeural Network Experiments on Personal Computers and Workstations (Bradford Books) (Paperback)\nby Granino A. Korn (ISBN 0262610736 )\n\nNote that the two books have a completely different aproach to neural networks. The second one (which is older) treats NN as transformation matrixes and provides algorithms for uptdating those (learning) but without a sound probabilistic reasoning support.\nThe first one goes from Bayesian probability (the effect of new information upon knowledge) to computing constructs for representing it.
353301
See p. 541 of Econometric Foundations by Mittelhammer, Judge and Miller.  Will be a bit tough to explain on Yahoo.  Actually, I don't like this book, but none of my other ones have this.
1122240
prend cotacte avec un detectuve ou avec les dirigents du club et essaye de faire une recherche sur le net
1121130
I hope this helps. here are a couple links to message boards discussing the Linden Method. the 3rd is a link to the Linden Method's website.
601657
Let me tell you a story about anecdotal evidence.  I had this friend who always supported his arguments with anecdotal evidence, and one day I took him to task for it, by presenting him with statistical data that completely demolished his hunches.  :^)
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As to part one, I can not recommend any acceptable, known test for such data (perhaps what I've written below might work).\n\nAs to part two, this is a horrid approximation since the midrange as the mean is a major assumption (even more far-reaching than assuming something about the populations' probability distributions).\n\nFor part 3 and also part 1:\n\nThe first problem you run into is that you do not know anything about the nature of the population distribution (normal, uniform, etc).  Thus, a nonparametric test would be required.  I know of no nonparametric tests for any of the statistics you have (minimum, maximum or range).\n\nIf it is possible to obtain the actual values for each of the samples in each set, the Kruskal-Wallis test should suffice.  It is nonparametric, so there are no problems with the requirement of assumptions about population distributions.\n\nFurther, its job is to determine if two samples are far enough apart such that the null hypothesis that the population distributions are the same can be rejected.  This would give you what you desire.\n\nAs far as your problem is concerned, the only statistics you have are minimum, maximum and range.   Perhaps one could derive a test to test the minima or maxima of each set, but one still runs into the problem that there can be no parameter placed on the population distribution.\n\nIf you can make an assumption about the distributions of the populations of interest (normal or whatnot), then it would be possible to derive such a test.  This would be a tedious task, however, as it would require thorough knowledge of order statistics and distribution functions.\n\nOne would need to use a test statistic based of off the difference in the two statistics that you use (I would use the maxima since they are further apart than the minima and probability distributions for ranges are uncharted territory unless you're a Ph.D.).  Thus, your test statistic is based off of the difference between the maxima (Y(n) - Z(n) where the (n) are subscripts, which is 2 in this case).\n\nTo perform this test, you would need to derive the probability distribution of the difference of the two maxima and use that to find the probability of the difference being two or greater.  This is quite a task for anyone and requires a large amount of knowledge in mathematical statistics.  I believe I could perform such a task, but it would likely take some time to complete.\n\nRemember though that you must make a critical assumption about the population before you can even do this.  I would recommend using the assumption of uniformity if you are unsure of the populations' distributions.  This is because the derived test would then be least likely to give a type I error, where you reject the data improperly, because the test is less likely to reject.
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It seems research on this has continued. Still sounds very preliminary though - experiments on small models or paper planes ;)  But maybe you'd like to check the links.
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I think you are talking about that book:\n\nOldrich Vasicek, "A test for normality based on sample entropy," Journal of the Royal Statistical Society, Series B, vol. 38, no. 1, pp. 54--59, 1976.\n\nI couldn't find a copy of this article, but it is easy to find a lot of other articles that are refer to it.\nHope, one of them will help you.\n\nAlso I strongly recommend to use a Citeseer scientific article database (2nd attached link). It usually contains a lot of information.
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Systems Applications and Products in Data Processing.
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http://en.wikipedia.org/wiki/Data_mining
65450
A few minutes of research and a basic understanding of probability should convince you that "messages" of any kind can be found in any sufficiently long piece of text using any kind of systematic text sampling technique.\n   By any kind of message, I mean just that. For any given statement, a statement that exactly contradicts the first statement is likely to be found along, as always, with a mind-boggling mountain of pure, random gibberish.\n\nAlthough it's best to determine these things for oneself, if an appeal to authority is convincing then here is a link to Caltech's mathematics department where 55 mathematicians have signed a document called, "Mathematicians' Statement on the Bible Codes"
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This is a good question even for a statistician.\nThe problem is your set is small and I do not know what the deviation between samples is, however the repeatability and reproducibility information can bind the window \nthrough which the third value will fall through or perhaps even remain in.  \n\nLet me know.
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There's a article here: \n"The cutoff phenomenon in finite Markov chains"\n\nhttp://www-stat.stanford.edu/~cgates/PERSI/papers/cutoff.pdf
17841
You might try DAP. You can find it here along with a lot of other free statistical software.\nhttp://members.aol.com/johnp71/javasta2.html
463145
binary generation or backtracking techniques
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Study of data with respect to different spectral responses