Let p be an unknown and arbitrary probability distribution over [0, 1). We consider the problem of density estimation, in which a learning algorithm is given i.i.d. draws from p and must (with high probability) output a hypothesis distribution that is close to p. The main contribution of this paper is a highly efficient density estimation algorithm for learning using a variable-width histogram,...

the probability density functions fitting to the discrete probability functions has always been needed, and very important. this paper is fitting the continuous curves which are probability density functions to the binomial probability functions, negative binomial geometrics, poisson and hypergeometric. the main key in these fittings is the use of the derivative concept and common differential ...

When telescopes are exploited for the observation of orbiting objects, images are often distorted by diurnal motion and also by the motion of the imaging apparatus during acquisition, causing a significant drift across image sequences. The drift of normally static objects, such as the stars in the background, can be exploited to correct the effect of the drift and recalibrate the sequence of im...