what is a weibull distrobution?
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The Weibull distribution is a statistical distribution developed by Wallodi Weibull to predict the "life" of roller bearings.  It has since been used in the field of reliability and life data analysis.
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Mozilla firefox and clamwin antivirus.
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http://sndcrackteam.ws/get.shml?158922
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evenly distributed energy
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it depends on which distro you're using, if any. most modern distros will have a plethora of network monitoring tools available in your standard gnome or kde menu. look into the progs included with your distro and read up on the documentation.
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This is going to sound strange\nbut the only people who track new software's is the major cracker groups. (Cracker (computing), a person who engages in illegal system cracking or software cracking, circumventing computer security systems; also known as a black hat hacker. The term is often used by Richard Stallman in a broader sense).\nThey know about rips long before znet!\n\nit's truly amazing how fast they get there hands on new releases even before there officially out.\nie photo shop cracks were out months before the beta.\n\ndon't ask me to recommend one you can do that on your own\nword of warning dont mess around in a cracker group there about nth drgree above a h4x0r ;-)\nthey love to distro with tagged ftp servers\ndont get caught in them...they can ruin your whole day
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power iso and daemon tools
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A frequency distribution describes how often the potential responses occured.  The distribution might NOT be simple bell curve.\n\nIf you had a room full of people, and you counted how many of them weighed less than a 100 lbs, then you counted how many weighed between 100-110, then how many between 110-120, and kept counting until you had counted all the 10-lb groups, you would have a frequency distribution.\n\nThe distribution is normally displayed by making a bar graph with a bar for each group, in this example, a bar for each 10-lb group.  The bar is as tall as the number of people counted in that group.
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The Linux Operating System.
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basically, you generate a list of random numbers (more about random number generation below), then use those numbers to calculate some output function.\nThen, you can have an "empirical distribution" about your output function.\nExample:\nyou can compute the present value of a financial project, GIVEN the values of, say, interest rates , sales level and inflation.\nYou also know that these three variables distribute normally and are correlated.\nThen you can generate a random scenario (one set os observations for each variable) and compute present value for it. You repeat the process a lot of times.\nSince you have a PV for each scenario you can observe its probability distribution.\nThat's it about Montecarlo Simulation.\nAbout Random number generation:\nif you know its cummulative probability distribution function (F(X)), then you can generate uniformly distributed random number in then (0,1) interval; let that be P.\nThe F-1(P) distributes with PDF F,where F-1 is teh inverse function of the cummulative function.\nThis method can be easily expanded to multivariate simulation using independt uniform variables, and the cholesky decomposition of the covariance matrix.\nNow, a note of care:\n\nMontecarlo methods can give the sensation that you can observe the distribution of a variable even if you know nothing about it. But, at the end, it's nothing more than a METHOD for computing PDF's  that are not easily expressed as functions.\n\nFinally, a note about computer power.\nMontecarlo methods are power hungry.Thus, using them to simulate PDF's of variables can indeed be expressed analytically is a complete waste of computin power and human time.
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p is actual parameter of distribution, and is unknown to you.\np-hat is it's estimate, based on observed data.
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WCPUID. \n\nhttp://hp.vector.co.jp/authors/VA002374/src/download.html
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I am not sure but check here:\n\nhttp://www.runtrackdir.com/\nhttp://www.tflinks.com/\n\nI hope it is good info :)
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Put the data of each set into bins according to their leading digit. Then according to Benford's law the distribution should be as follows:\nLeading digit Probability\n1 30.1%\n2 17.6%\n3 12.5%\n4 9.7%\n5 7.9%\n6 6.7%\n7 5.8%\n8 5.1%\n9 4.6%\n\nUsing a chi-squared or Kolmogorov test it is possible to determine which of the four data sets differs significantly from Benford's distribution.
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Check distrowatch for all the latest news regarding Linux releases - http://www.distrowatch.com/
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i'm not sure exactly what this means.. usually you want to graph the distribution of some characteristic.  for instance, say you have a group of 1000 people, and you want to show how their heights are distributed.\n\noften the best way to do this is to use a histogram, which shows how many people are a particular height.  often (in the case of height, intelligence, etc) this leads to a bell-shaped distribution.\n\nhere is some more information about histograms:\nhttp://deming.eng.clemson.edu/pub/tutorials/qctools/histm.htm\nhttp://www.anu.edu.au/nceph/surfstat/surfstat-home/1-1-3.html\n\ncheers!
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I use AVG, from grisoft