If some data defeats every method you've tried

To determine its structural form, and you're yearning

To find every pattern that's hidden inside

So that you can achieve perfect MDL learning,

To squeeze from your data the meaningful juice,

And to throw away all the superfluous rind

The complexity measure that's perfect to use

Is the one that Andrei Kolmogorov defined.

Let the data be D. Write the minimal code

That will print out the data eventually.

And now tally the length of the program you wrote,

For that sum is the value they call K(D)! (read "K of D")

There's an obstacle, though, that you'll find if you try, which

Is pretty much certain to get in the way

Of applying this thought of Andrei Nikolaevich:

If D is too large, then you can't compute K.

This is part of the collection Verses for the Information Age by Ernest Davis