the problem of estimating the parameter ?, when it is restricted to an interval of the form , in a class of discrete distributions, including binomial negative binomial discrete weibull and etc., is considered. we give necessary and sufficient conditions for which the bayes estimator of with respect to a two points boundary supported prior is minimax under squared log error loss function. for s...

Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent’s parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and req...

This paper presents a problem reduction scheme that converts geometric constraints in work space to a system of equations in parameter space. We demonstrate that this scheme can solve many interesting geometric problems that have been considered quite di cult to deal with using conventional techniques. An important advantage of our approach is that equations represented in the parameter space h...

In this paper a method is presented that parameterizes orthogonal wavelet transforms with respect to their properties (i.e. compact support, vanishing moments, regularity , symmetry) and also takes into considerations a simple implementation of the transform. The parameter space is given by the rotation angles of the orthogonal 2 2 rotations used in the lattice lters that realize the diierent s...

Multiobjective optimization using the conventional weighted aggregation of the objectives method is known to have several drawbacks. In this paper, multiobjective optimization using the weighted aggregation method is approached with the help of evolutionary algorithms. It is shown through a number of test functions that a Pareto front can be achieved from one single run of evolutionary optimiza...