We show, starting from first principles, that thermodynamics’ first law can be microscopically obtained for Fisher's information measure without need of invoking the adiabatic theorem. Further, it is proved that enforcing the Fisher-first law requirements in a process in which the probability...

We present an expression of the economic concept of asymmetric information with which it is possible to derive the dynamical laws of an economy. To illustrate the utility of this approach we show how the assumption of optimal information flow leads to a general class of investment strategies...

We present a thermodynamic formulation for scale-invariant systems based on the minimization with constraints of the Fisher information measure. In such a way a clear analogy between these systems’ thermal properties and those of gases and fluids is seen to emerge in a natural fashion. We...

We show that thermodynamic uncertainties (TU) preserve their form in passing from Boltzmann–Gibbs’ statistics to Tsallis’ one provided that we express these TU in terms of the appropriate variable conjugate to the temperature in a nonextensive context.

The optimal sensor placement problem involves determining the most effective locations to place a network of sensors across an array of measurable signals, in accordance with a set of specified objectives and constraints, such as cost, performance, and sensitivity to variations in uncertain...

We show how the demon of Frieden and Soffer, working in a nonextensive statistical scenario, is able to devise solutions to some of Einstein’s field equations by recourse to nonlocal changes of variables between appropriate differential equations. It is seen that a variety of cosmological...

In this paper, a new class of skew distributions containing both unimodal as well as bimodal distributions with flexible hazard rate behavior is introduced. Several distributional properties of this class are investigated. A characterization, a generating method and parameter estimation are also...

The probit function is an alternative transformation to the logistic function in the analysis of binary data. However, use of the probit function is prohibitively complicated for cases of multivariate or repeated-measure binary responses, as integrations involving the multivariate normal...