This note provides necessary and su cient conditions for third-degree stochastic dominance between the probability measures on a nite grid of n points. Verifying these conditions involve n linear and n quadratic inequalities; the arithmetic operation of division is not required. The results are compared with those of Fishburn and Lavalle, Math. Operat. Res. 20 (1995) 513–525 and Milne and Neave...

Lifted probabilistic inference (Poole, 2003) and symbolic dynamic programming for lifted stochastic planning (Boutilier et al, 2001) were introduced around the same time as algorithmic efforts to use abstraction in stochastic systems. Over the years, these ideas evolved into two distinct lines of research, each supported by a rich literature. Lifted probabilistic inference focused on efficient ...

Monte Carlo simulation is a very important class of stochastic methods for calculating thermal properties of many-particle systems—arguably these are the most important numerical techniques in statistical physics. Monte Carlo simulation methods are related to the elementary Monte Carlo integration methods that we discussed earlier, but are based on more efficient non-uniform sampling schemes. B...

Pseudorandom number generators are required for many computational tasks, such as stochastic modelling and simulation. This paper investigates the serial CPU and parallel GPU implementation of a Linear Congruential Generator based on the binary representation of the normal number α2,3. We adapted two methods of modular reduction which allowed us to perform most operations in 64-bit integer arit...

Monte Carlo simulation is a very important class of stochastic methods for calculating thermal properties of many-particle systems—arguably these are the most important numerical techniques in statistical physics. Monte Carlo simulation methods are related to the elementary Monte Carlo integration methods that we discussed earlier, but are based on more efficient non-uniform sampling schemes. B...

People employ the function-on-function regression to model relationship between two stochastic processes. Fitting this model, widely used strategies include functional partial least squares algorithms which typically require iterative eigen-decomposition. Here we introduce a route of based upon Krylov subspace. Our can be expressed in forms equivalent each other exact arithmetic: One is non-ite...

Operating in energy and commodity markets require a management of risk using derivative products such as forward futures, well options on these. Many the popular stochastic models for spot dynamics weather variables developed from empirical studies belong to class polynomial jump diffusion processes. We derive tailor-made framework efficient approximation main derivatives encountered markets, e...

Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recogni...

Stochastic approximation methodology is applied to the problem of estimating an optimal block replacement policy. Several improvements to minimize the asymptotic mean square error of the estimator are considered. A stopping time is then defined with the objective of a fixed-width confidence interval for the optimal policy. A nonparametric estimator is used for sequential estimation of the renew...

Data from human subjects recorded by an eyetracker while they are learning new faces suggests face recognition can be modeled as a sequential stochastic process, where the underlying observations at a given fixation depend on both foveal and parafoveal information. In contrast to most pattern recognition based approaches, this foveal face recognition approach is incremental and scalable to larg...