background: monte carlo and experimental relative dose determination in a water phantom, due to a high dose rate (hdr) 192ir source is presented for real energy spectrum and monochromatic at 356 kev. materials and methods: the dose distribution has been calculated around the 192ir located in the center of 30 cm ×30 cm ×30 cm water phantom using mcnp4c code by monte carlo method. relative dose v...

Many applications require approximate values of path integrals. A typical approach is to approximate the path integral by a high dimensional integral and apply a Monte Carlo (randomized) algorithm. However, Monte Carlo algorithm requires roughly " ?2 integrand evaluations to provide an "-approximation. Moreover, the error bound of " is guaranteed only in a stochastic sense. Do we really need to...

Inference for a complex system with a rough energy landscape is a central topic in Monte Carlo computation. Motivated by the successes of the Wang–Landau algorithm in discrete systems, we generalize the algorithm to continuous systems. The generalized algorithm has some features that conventional Monte Carlo algorithms do not have. First, it provides a new method for Monte Carlo integration bas...

The stochastic approximation Monte Carlo (SAMC) algorithm has recently been proposed as a dynamic optimization algorithm in the literature. In this paper, we show in theory that the samples generated by SAMC can be used for Monte Carlo integration via a dynamically weighted estimator by calling some results from the literature of nonhomogeneous Markov chains. Our numerical results indicate that...

In this paper we analyze a quasi-Monte Carlo method for solving systems of linear algebraic equations. It is well known that the convergence of Monte Carlo methods for numerical integration can often be improved by replacing pseudorandom numbers with more uniformly distributed numbers known as quasirandom numbers. Here the convergence of a Monte Carlo method for solving systems of linear algebr...

This paper presents a new method that combines quasi-Monte Carlo quadrature with importance sampling to solve the general rendering equation efficiently. Since classical importance sampling has been proposed for Monte-Carlo integration, first an appropriate formulation is elaborated for deterministic sample sets used in quasi-Monte Carlo methods. This formulation is based on integration by vari...

The central computational problem in Bayesian analysis is that of computing the posterior expectation of one or more quantities of interest|a ratio of two often-intractable integrals. The method of Laplace gives an approximation to this ratio that depends only on asymptotic features of the function of interest and the posterior density function, in a neighborhood of the posterior mode. Monte Ca...

Monte Carlo computer programming is becoming increasingly popular to those who use it, due to the ease with which complex problems may be formulated and solved. However, the growth of MC programming for small projects is inhibited by a frequent misconception of difficulty, inferred from the high level of complexity of problems solved inHigh Energy and Nuclear Physics using MC methods. Inadditio...

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...

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...