The introduction to Probability Theory: The Logic of Science has been useful for explaining what various statistical procedures are used for when making an inference about data.
Maximum entropy is a technique used to establish probabilities for outcomes from data given no prior information or assumptions. It is essentially an algorithm that comes to a conclusion without any bias from the experimenter. Bayesian techniques, on the other hand, require some prior information, and this will affect the conclusion.
Typically, when performing acts of inference, one begins with maximum entropy if very little is known except what's given in the data. Once more is known, one may turn to Bayesian analysis.
Bayesian analysis requires five things: a model, sample space, hypothesis space, prior probabilities, and sampling distributions.
There is much work to be done in developing techniques when even little is known about the raw data; this could lead to steps that can assist in studies where even maximum entropy may fail to give adequate results.
A "lab notebook" about my life in science, programming, and philosophy.
