1 I. About the distribution tables 2 3 The table used for "synthesizing" the distribution is essentially a scaled, 4 translated, inverse to the cumulative distribution function. 5 6 Here's how to think about it: Let F() be the cumulative distribution 7 function for a probability distribution X. We'll assume we've scaled 8 things so that X has mean 0 and standard deviation 1, though that's not 9 so important here. Then: 10 11 F(x) = P(X <= x) = \int_{-inf}^x f 12 13 where f is the probability density function. 14 15 F is monotonically increasing, so has an inverse function G, with range 16 0 to 1. Here, G(t) = the x such that P(X <= x) = t. (In general, G may 17 have singularities if X has point masses, i.e., points x such that 18 P(X = x) > 0.) 19 20 Now we create a tabular representation of G as follows: Choose some table 21 size N, and for the ith entry, put in G(i/N). Let's call this table T. 22 23 The claim now is, I can create a (discrete) random variable Y whose 24 distribution has the same approximate "shape" as X, simply by letting 25 Y = T(U), where U is a discrete uniform random variable with range 1 to N. 26 To see this, it's enough to show that Y's cumulative distribution function, 27 (let's call it H), is a discrete approximation to F. But 28 29 H(x) = P(Y <= x) 30 = (# of entries in T <= x) / N -- as Y chosen uniformly from T 31 = i/N, where i is the largest integer such that G(i/N) <= x 32 = i/N, where i is the largest integer such that i/N <= F(x) 33 -- since G and F are inverse functions (and F is 34 increasing) 35 = floor(N*F(x))/N 36 37 as desired. 38 39 II. How to create distribution tables (in theory) 40 41 How can we create this table in practice? In some cases, F may have a 42 simple expression which allows evaluating its inverse directly. The 43 pareto distribution is one example of this. In other cases, and 44 especially for matching an experimentally observed distribution, it's 45 easiest simply to create a table for F and "invert" it. Here, we give 46 a concrete example, namely how the new "experimental" distribution was 47 created. 48 49 1. Collect enough data points to characterize the distribution. Here, I 50 collected 25,000 "ping" roundtrip times to a "distant" point (time.nist.gov). 51 That's far more data than is really necessary, but it was fairly painless to 52 collect it, so... 53 54 2. Normalize the data so that it has mean 0 and standard deviation 1. 55 56 3. Determine the cumulative distribution. The code I wrote creates a table 57 covering the range -10 to +10, with granularity .00005. Obviously, this 58 is absurdly over-precise, but since it's a one-time only computation, I 59 figured it hardly mattered. 60 61 4. Invert the table: for each table entry F(x) = y, make the y*TABLESIZE 62 (here, 4096) entry be x*TABLEFACTOR (here, 8192). This creates a table 63 for the ("normalized") inverse of size TABLESIZE, covering its domain 0 64 to 1 with granularity 1/TABLESIZE. Note that even with the granularity 65 used in creating the table for F, it's possible not all the entries in 66 the table for G will be filled in. So, make a pass through the 67 inverse's table, filling in any missing entries by linear interpolation. 68 69 III. How to create distribution tables (in practice) 70 71 If you want to do all this yourself, I've provided several tools to help: 72 73 1. maketable does the steps 2-4 above, and then generates the appropriate 74 header file. So if you have your own time distribution, you can generate 75 the header simply by: 76 77 maketable < time.values > header.h 78 79 2. As explained in the other README file, the somewhat sleazy way I have 80 of generating correlated values needs correction. You can generate your 81 own correction tables by compiling makesigtable and makemutable with 82 your header file. Check the Makefile to see how this is done. 83 84 3. Warning: maketable, makesigtable and especially makemutable do 85 enormous amounts of floating point arithmetic. Don't try running 86 these on an old 486. (NIST Net itself will run fine on such a 87 system, since in operation, it just needs to do a few simple integral 88 calculations. But getting there takes some work.) 89 90 4. The tables produced are all normalized for mean 0 and standard 91 deviation 1. How do you know what values to use for real? Here, I've 92 provided a simple "stats" utility. Give it a series of floating point 93 values, and it will return their mean (mu), standard deviation (sigma), 94 and correlation coefficient (rho). You can then plug these values 95 directly into NIST Net. 96
