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You can derive the formula for this using the Chinese Remainder Theorem.

Consider the problem of finding a polynomial p(x) such that p(x_i) = y_i for some finite set of (x_i) and (y_i). The classic approach to this is Lagrange interpolation, which can be seen as a special case of the Chinese Remainder Theorem. Essentially, you are trying to find a p which satisfies the following modular congruences:

  p(x) == y_i (mod (x - x_i))
The constructive proof of CRT, specialised to this problem, yields an algorithm equivalent to Lagrange interpolation. To extend Lagrange interpolation so as to include derivative information y'_i at each x_i, change that congruence to:

  p(x) == y_i + y'_i (x - x_i) (mod (x - x_i)^2)
And to give second derivative information, change the modular congruence to:

  p(x) == y_i + y'_i (x - x_i) + y''_i (x - x_i)^2 / 2! (mod (x - x_i)^3)
And so on...
(comment deleted)
Bezier curves are actually my favorite way to teach recursion. They give a nice visual representation of what's happening, the base case is pretty straight forward, and so is the math. The time-step element adds just enough extra complexity to take it beyond "let's sum a list" or "check if a string is a palindrome".