Abstract
The measurement and/or storage of high order probability distributions implies exponential increases in equipment complexity. This paper considers the possibility of storing several of the lower order component distributions and using this partial information to form an approximation to the actual high order distribution. The approximation method is based on an information measure for the “closeness” of two distributions and on the criterion of maximum entropy. Approximations consisting of products of appropriate lower order distributions are proved to be optimum under suitably restricted conditions. Two such product approximations can be compared and the better one selected without any knowledge of the actual high order distribution other than that implied by the lower order distributions.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
