A rate equation for a discrete probability distribution is discussed as a route to describe smooth relaxation towards the maximum entropy distribution compatible at all times with one or more linear constraints. The resulting dynamics follows the path of steepest entropy ascent compatible with the constraints. The rate equation is consistent with the Onsager theorem of reciprocity and the fluct...

Maximum entropy models are often used to describe supply and demand behavior in urban transportation and land use systems. However, they have been criticized for not representing behavioral rules of system agents and because their parameters seems to adjust only to modeler-imposed constraints. In response, it is demonstrated that the solution to the entropy maximization problem with linear cons...

In this paper the relevance of holographic entropy bounds in the context of inflation is investigated. We distinguish between entropy on large and small scales and confront the entropy of quantum fluctuations in an inflating cosmology with the appropriate entropy bounds. In conclusion we do not find any constraints on inflation from holography, but some suggestions for future studies are given.

In this paper the relevance of holographic entropy bounds in the context of inflation is investigated. We distinguish between entropy on large and small scales and confront the entropy of quantum fluctuations in an inflating cosmology with the appropriate entropy bounds. In conclusion we do not find any constraints on inflation from holography, but some suggestions for future studies are given.

The concept of uncertain entropy is used to provide a quantitative measurement of the uncertainty associated with uncertain variables. After introducing the definition, this paper gives some examples of entropy of uncertain variables. Furthermore this paper proposes the maximum entropy principle for uncertain variables, that is, out of all the uncertainty distributions satisfying given constrai...

Recently the author developed a numerical method for the multidimensional momentconstrained maximum entropy problem, which is practically capable of solving maximum entropy problems in the two-dimensional domain with moment constraints of order up to 8, in the threedimensional domain with moment constraints of order up to 6, and in the four-dimensional domain with moment constraints of order up...

Maximum-entropy (ME) inference of state probabilities using state-dependent constraints is popular in the study of complex systems. In stochastic systems, how state space topology and path-dependent constraints affect ME-inferred state probabilities remains unknown. To that end, we derive the transition probabilities and the stationary distribution of a maximum path entropy Markov process subje...

Given the three-dimensional structure of a protein, its thermodynamic properties are calculated using a recently introduced distance constraint model (DCM) within a mean-field treatment. The DCM is constructed from a free energy decomposition that partitions microscopic interactions into a variety of constraint types, i.e., covalent bonds, salt-bridges, hydrogen-bonds, and torsional-forces, eac...

The general problem of inductive inference is to update from a prior probability distribution to a posterior distribution when new information becomes available. Bayes' rule is the natural way to update when the new information is in the form of data while Jaynes’ method of maximum entropy, MaxEnt, is designed to handle information in the form of constraints. However, the range of applicability...