Adaptive importance sampling (AIS) methods are increasingly used for the approximation of distributions and related intractable integrals in context Bayesian inference. Population Monte Carlo (PMC) algorithms a subclass AIS methods, widely due to their ease adaptation. In this paper, we propose novel algorithm that exploits benefits PMC framework includes more efficient adaptive mechanisms, exp...

Any search or sampling algorithm for solution of inverse problems needs guidance to be efficient. Many algorithms collect and apply information about the problem on fly, much improvement has been made in this way. However, as a consequence No-Free-Lunch Theorem, only way we can ensure significantly better performance is build possible. In special case Markov Chain Monte Carlo (MCMC) review how ...

In this lecture, we cover the other major method for generating atomic trajectories: the Monte Carlo (MC) approach. Unlike MD, Monte Carlo methods are stochastic in nature—the time progression of the atomic positions proceeds randomly and is not predictable given a set of initial conditions. The dynamic principles by which we evolve the atomic positions incorporate random moves or perturbations...

Quasi-Monte Carlo methods are a variant of ordinary Monte Carlo methods that employ highly uniform quasirandom numbers in place of Monte Carlo’s pseudorandom numbers. Monte Carlo methods offer statistical error estimates; however, while quasi-Monte Carlo has a faster convergence rate than normal Monte Carlo, one cannot obtain error estimates from quasi-Monte Carlo sample values by any practical...

Monte Carlo applications are widely perceived as computationally intensive but naturally parallel. Therefore, they can be effectively executed on the grid using the dynamic bag-of-work model. This paper concentrates on analyzing the characteristics of large-scale Monte Carlo computation for grid computing. Based on these analyses, we improve the efficiency of the subtask-scheduling scheme by im...

Monte Carlo applications are widely perceived as computationally intensive but naturally parallel. Therefore, they can be effectively executed on the Grid using the dynamic bag-of-work model. In this paper we concentrate on analyzing the characteristics of large-scale Monte Carlo computation for Grid computing. Based on these analyses, we improve the efficiency of the subtask-scheduling scheme ...

Reading the Principia [1] it is easy to realize that Newton, without being aware of the fact, has corrected the law of the fall of bodies formulated by Galilei. In the present paper we examine the consequences of such a fact and show that they affect the entire Celestial Mechanics, being related to the very small 43” per century of the perihelion shift of Mercury as well as to the substantial 5...

ABSTRACT Background: accurate methods of radiation therapy dose calculation. There are different Monte Carlo codesfor simulation of photons, electrons and the coupled transport of electrons and photons. MCNPis a general purpose Monte Carlo code that can be used for electron, photon and coupledphoton-electron transport.Monte Carlo simulation of radiation transport is considered to be one of the ...