Maximum entropy and related methods

Originally coming from physics, maximum entropy (ME) has been promoted to a general principle of inference primarily by the works of Jaynes. ME applies to the problem of inferring a probability mass (or density) function, or any non-negative function p(x), when the available information specifies a set E of feasible functions, and there is a prior guess q ∉ E. The author will review the arguments that have been put forward for justifying ME. In this author´s opinion, the strongest theoretical support to ME is provided by the axiomatic approach. This shows that, in some sense, ME is the only logically consistent method of inferring a function subject to linear constraints