Monte Carlo methods are widely used for approximating complicated, multidimensional integrals Bayesian inference. Population (PMC) is an important class of methods, which adapts a population proposals to generate weighted samples that approximate the target distribution. When distribution expensive evaluate, PMC may encounter computational limitations since it requires many evaluations To addre...

In this paper we investigate an efficient implementation of the Monte Carlo EM algorithm based on Quasi-Monte Carlo sampling. The Monte Carlo EM algorithm is a stochastic version of the deterministic EM (Expectation-Maximization) algorithm in which an intractable E-step is replaced by a Monte Carlo approximation. Quasi-Monte Carlo methods produce deterministic sequences of points that can signi...

As a result of the failure of the foundational programs, in the past three decades there has been a revival in the empiricist philosophy of mathematics. In the latter several trends can be distinguished and among them an eminent position is occupied by a Millian one. A distinctive feature of the supporters of such trend is that, while considering the absolute certainty of mathematical propositi...

background: one of the main problems of dedicated iort accelerators is to determine dosimetric characteristics of the electron beams. monte carlo simulation of iort accelerator head and produced beam will be useful to improve the accuracy of beam dosimetry. materials and methods: liac accelerator head was modeled using the beamnrcmonte carlo simulation system. phase-space files were generated a...

background: inclusion of inhomogeneity corrections in intensity modulated small fields always makes conformal irradiation of lung tumor very complicated in accurate dose delivery. objective: in the present study, the performance of five algorithms via monte carlo, pencil beam, convolution, fast superposition and superposition were evaluated in lung cancer intensity modulated radiotherapy planni...