Let me explain a few things about it to you.
There's this page called Google. It's at
http://www.google.com/ -- now if you go there, and search for "principle maximum ignorance" the first page that it will lead you to is the Wikipedia page on the principle of maximum entropy. That can be found
right here.
As you can easily read, it says: "The
principle of maximum entropy states that...the
probability distribution which best represents the current state of knowledge is the one with largest
entropy."
It furthermore states that "The principle was first expounded by
E. T. Jaynes in two papers in 1957" and right next to that are two blue numbers. Now, if you click either (or both) of those numbers, you will find yourself linked to the original papers written by Edwin, which can be found
here and
here.
Now if either of the papers is too thick for you, you can just go back to the Wikipedia article and get some background information about it. Or you can read
Edwin's book, which is available online for free. Most of the answers you are looking for can be found on page 14.
QUOTE:
...before Bayesian methods can be used, a problem must be developed beyond the “exploratory phase” to the point where it has enough structure to determine all the needed apparatus (a model, sample space, hypothesis space, prior probabilities, sampling distribution). Almost all scientific problems pass through an initial exploratory phase in which we have need for inference, but the frequentist assumptions are invalid and the Bayesian apparatus is not yet available. Indeed, some of them never evolve out of the exploratory phase. Problems at this level call for more primitive means of assigning probabilities directly out of our incomplete information. For this purpose,
the Principle of Maximum Entropy has at present the clearest theoretical justification and is the most highly developed computationally, with an analytical apparatus as powerful and versatile as the Bayesian one. To apply it we must define a sample space, but do not need any model or sampling distribution. In effect, entropy maximization creates a model for us out of our data, which proves to be optimal by so many different criteria [(1) The model created is the simplest one that captures all the information in the constraints (Chapter 11); (2) It is the unique model for which the constraints would have been sufficient statistics (Chapter 8); (3) If viewed as constructing a sampling distribution for subsequent Bayesian inference from new data D, the only property of the measurement errors in D that are used in that subsequent inference are the ones about which that sampling distribution contained some definite prior information (Chapter 7). Thus the formalism automatically takes into account all the information we have, but avoids assuming information that we do not have. This contrasts sharply with orthodox methods, where one does not think in terms of information at all, and in general violates both of these desiderata] that it is hard to imagine circumstances where one would not want to use it in a problem where we have a sample space but no model.
ENDQUOTE
Really, I'm surprised that anyone who claims to know about science hasn't at least
HEARD of the principle. It's not like I'm pulling this stuff out of my derriere you know.