1. A computer-implemented method for classifying a computer file as malware or whiteware, comprising the steps of: (a) initializing a malware belief (P(M)) to a predetermined value; (b) accessing, using one or more processors, from a database of previously determined observable features, an observable feature from the computer file; (c) obtaining a malware conditional probability for the accessed observable feature from a database of predetermined probabilities for malware; (d) obtaining a whiteware conditional probability for the accessed observable feature from a database of predetermined probabilities for whiteware; (e) applying Bayes theorem to calculate a probability that the computer file is malware given the malware and whiteware conditional probabilities of the accessed observable feature; (f) updating the malware belief (P(M)) with a result of Bayes theorem calculated with respect to the accessed observable feature; (g) iterating steps (c) through (f) for all previously determined observable features; (h) setting the last updated malware belief (P(M)) as a final malware belief (P(M)Final); and, (i) evaluating the final malware belief (P(M)Final) to classify the computer file as malware if the final malware belief (P(M)Final) is greater than or equal to a predetermined threshold value, or as whiteware if the final malware belief (P(M)Final) is less than the same predetermined threshold value; and (j) evaluating the malware belief (P(M)) after each iteration and ceasing iterating by setting the last updated malware belief (P(M)) as the final malware (P(M)) value if: (i) the malware belief (P(M)) is greater than or equal to a predetermined convergence value, wherein the computer file is classified as malware; or, (ii) the malware belief (P(M)) is less than the same predetermined convergence value, wherein the computer file is classified as whiteware.