............................................................................................................................................................................ 8 Introduction ...................................................................................................................................................................... 9 Olive ridley sea turtles in the eastern ...

In this paper, we propose a simple approach for approximating small sample distributions based on the method of Maximum Entropy (maxent) density. Unlike previous studies that use the maxent method as a curve-fitting tool, we focus on the “small sample asymptotics” of the approximation. We show that this method obtains the same asymptotic order of accuracy as that of the classical Edgeworth expa...

Maximum entropy (maxent) approach, formally equivalent to maximum likelihood, is a widely used density-estimation method. When input datasets are small, maxent is likely to overfit. Overfitting can be eliminated by various smoothing techniques, such as regularization and constraint relaxation, but theory explaining their properties is often missing or needs to be derived for each case separatel...

Maximum entropy (Maxent) is useful in many areas. Iterative scaling (IS) methods are one of the most popular approaches to solve Maxent. With many variants of IS methods, it is difficult to understand them and see the differences. In this paper, we create a general and unified framework for IS methods. This framework also connects IS and coordinate descent (CD) methods. Besides, we develop a CD...

Through completing an underspeciied probability model, Maximum Entropy (MaxEnt) supports non-monotonic inferences. Some major aspects of how this is done by MaxEnt can be understood from the background of two principles of rational decision: the concept of Indiierence and the concept of Independence. In a formal speciication MaxEnt can be viewed as (conservative) extension of these principles; ...

Ill-posed inverse problems of the form y = Xp where y is J-dimensional vector of a data, p is m-dimensional probability vector which can not be measured directly and matrix X of observable variables is a known J ×m matrix, J < m, are frequently solved by Shannon’s entropy maximization (MaxEnt, ME). Several axiomatizations were proposed (see for instance [1], [2], [3], [4], [5], [6], [7], [8], a...