Maximum Entropy Estimation of Probability Distribution of Variables in Higher Dimensions from Lower Dimensional Data

Entropy

Jayajit Das¹  Sayak Mukherjee¹  Susan E. Hodge¹ 

[1] Battelle Center for Mathematical Medicine, Research Institute at the Nationwide Children’s Hospital, 700 Children’s Drive, OH 43205, USA; E-Mails:

关键词: maximum entropy;    joint probability distribution;    microbial ecology;   

DOI  :  10.3390/e17074986

来源: mdpi

【 摘 要 】

A common statistical situation concerns inferring an unknown distribution Q(x) from a known distribution P(y), where X (dimension n), and Y (dimension m) have a known functional relationship. Most commonly, n ≤ m, and the task is relatively straightforward for well-defined functional relationships. For example, if Y₁ and Y₂ are independent random variables, each uniform on [0, 1], one can determine the distribution of X = Y₁ + Y₂; here m = 2 and n = 1. However, biological and physical situations can arise where n > m and the functional relation Y→X is non-unique. In general, in the absence of additional information, there is no unique solution to Q in those cases. Nevertheless, one may still want to draw some inferences about Q. To this end, we propose a novel maximum entropy (MaxEnt) approach that estimates Q(x) based only on the available data, namely, P(y). The method has the additional advantage that one does not need to explicitly calculate the Lagrange multipliers. In this paper we develop the approach, for both discrete and continuous probability distributions, and demonstrate its validity. We give an intuitive justification as well, and we illustrate with examples.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland