We show that random DNF formulas, random log-depth decision trees and random deterministic finite acceptors cannot be weakly learned with a polynomial number of statistical queries with respect to an arbitrary distribution.

Performance evaluation of electricity distribution units is an important issue between researchers and regulators. Classic Data Envelopment Analysis models with deterministic data have been used by many authors to measure efficiency of power distribution units in different countries. However, Data Envelopment Analysis with stochastic data are rarely used to measure efficiency of distribution co...

Objective: The current research is to investigate the relation between deterministic thinking and mental health among drug abusers, in which the role of  cognitive distortions is considered and clarified by focusing on deterministic thinking. Methods: The present study is descriptive and correlative. All individuals with experience of drug abuse who had been referred to the Shafagh Reha...

Present quantum theory, which is statistical in nature, does not predict joint probability distribution of position and momentum because they are noncommuting. We propose a deterministic quantum theory which predicts a joint probability distribution such that the separate probability distributions for position and momentum agree with usual quantum theory. Unlike the Wigner distribution the sugg...

Maximum Likelihood Estimation (MLE) is a widely used statistical estimation method. In this lecture, we will study its properties: efficiency, consistency and asymptotic normality. MLE is a method for estimating parameters of a statistical model. Given the distribution of a statistical model f(y ; θ) with unkown deterministic parameter θ, MLE is to estimate the parameter θ by maximizing the pro...

Two-qubit quantum codes have been suggested to obtain better efficiency and higher loss tolerance in quantum key distribution. Here, we propose a two-qubit quantum key distribution protocol based on a mixed basis consisting of two Bell states and two states from the computational basis. All states can be generated from a single entangled photon pair resource by using local operations on only on...

Stochastic programming is a valuable optimization tool where used when some or all of the design parameters of an optimization problem are defined by stochastic variables rather than by deterministic quantities. Depending on the nature of equations involved in the problem, a stochastic optimization problem is called a stochastic linear or nonlinear programming problem. In this paper,a stochasti...