1. A method for reducing use of runtime memory in a data processing algorithm that uses one or more probability distribution functions, where each probability distribution function is characterized by one or more uncompressed mean values and one or more variance values, wherein the uncompressed mean and variance values are represented by α-bit floating point numbers, where a is an integer greater than 1, the method comprising the steps of: converting the probability distribution functions to compressed probability functions having compressed mean and/or variance values represented as β-bit integers, where β is less than α, whereby the compressed mean and/or variance values occupy less memory space than the uncompressed mean and/or variance values; and performing one or more portions of the data processing algorithm with the compressed mean and variance values.