Approximating probability distributions using small sample spaces

We formulate the notion of a "good approximation" to a probability distribution over a finite abelian group double-struck G sign. The quality of the approximating distribution is characterized by a parameter ε which is a bound on the difference between corresponding Fourier coefficients of the two distributions. It is also required that the sample space of the approximating distribution be of size polynomial in log |double-struck G sign| and 1/ε. Such approximations are useful in reducing or eliminating the use of randomness in certain randomized algorithms. We demonstrate the existence of such good approximations to arbitrary distributions. In the case of n random variables distributed uniformly and independently over the range {0, . . ., d - 1}, we provide an efficient construction of a good approximation. The approximation constructed has the property that any linear combination of the random variables (modulo d) has essentially the same behavior under the approximating distribution as it does under the uniform distribution over {0, . . ., d - 1}. Our analysis is based on Weil's character sum estimates. We apply this result to the construction of a non-binary linear code where the alphabet symbols appear almost uniformly in each non-zero code-word.