The concept of C-space entropy was recently introduced in [1, 2, 3], as a measure of knowledge of C-space for sensor-based path planning and exploration for general robot-sensor systems. The robot plans the next sensing action to maximally reduce the expected C-space entropy, also called the maximal expected entropy reduction, or MER criterion. The expected C-space entropy computation, however,...

We consider the maximum entropy problems associated with Rényi Q-entropy, subject to two kinds of constraints on expected values. The constraints considered are a constraint on the standard expectation, and a constraint on the generalized expectation as encountered in nonextensive statistics. The optimum maximum entropy probability distributions, which can exhibit a power-law behaviour, are der...

We discuss the curvaton scenario with the Affleck-Dine baryogenesis. In this scenario, non-vanishing baryonic entropy fluctuation may be generated even without primordial fluctuation of the Affleck-Dine field. Too large entropy fluctuation is inconsistent with the observations and hence constraints on the curvaton scenario with the Affleck-Dine baryogenesis are obtained. We calculate the baryon...

In this paper a non-stationary processes that tend to maximize the Tsallis entropy are considered. Systems with discrete probability distribution for the Tsallis entropy have already been investigated on the basis of the Speed-Gradient principle. The evolution of probability density function and continuous form of the Tsallis entropy are considered. A set of equations describing dynamics of a s...

Given a fuzzy variable, what is the degree of difficulty of predicting the specified value that the fuzzy variable will take? Entropy provides a measure to characterize the fuzziness. Given some constraints, there are usually multiple compatible membership functions. Which membership function shall we take? The maximum entropy principle attempts to select the membership function that maximizes ...

We establish a mistake bound for an ensemble method for classification based on maximizing the entropy of voting weights subject to margin constraints. The bound is the same as a general bound proved for the Weighted Majority Algorithm, and similar to bounds for other variants of Winnow. We prove a more refined bound that leads to a nearly optimal algorithm for learning disjunctions, again, bas...

In this paper, we present a maximum entropy (maxent) approach to the fusion of experts opinions, or classifiers outputs, problem. Themaxent approach is quite versatile and allows us to express in a clear, rigorous,way the a priori knowledge that is available on the problem. For instance, our knowledge about the reliability of the experts and the correlations between these experts can be easily ...

In this paper, we present a maximum entropy (maxent) approach to the fusion of experts opinions, or classifiers outputs, problem. The maxent approach is quite versatile and allows us to express in a clear, rigorous, way the a priori knowledge that is available on the problem. For instance, our knowledge about the reliability of the experts and the correlations between these experts can be easil...

We consider the phenomenon of entropy concentration under linear constraints in a discrete setting, using the “balls and bins” paradigm, but without the assumption that the number of balls allocated to the bins is known. Therefore instead of frequency vectors and ordinary entropy, we have count vectors with unknown sum, and a certain generalized entropy. We show that if the constraints bound th...

The set of solutions inferred by the generic maximum entropy (MaxEnt) or maximum relative entropy (MaxREnt) principles of Jaynes – considered as a function of the moment constraints or their conjugate Lagrangian multipliers – is endowed with a Riemannian geometric description, based on the second differential tensor of the entropy or its Legendre transform (negative Massieu function). The analy...