Markov chains are among the simplest stochastic processes, just one step beyond iid sequences of random variables. Traditionally they’ve been used in modelling a variety of physical phenomena, but recently interest has grown enormously due to their applicability in facilitating Bayesian computation. These lecture notes and lectures are intended to introduce the elements of markov chain theory, ...

The Monte Carlo (MC) Hamiltonian is a new stochastic method to solve manybody problems. The MC Hamiltonian represents an effective Hamiltonian in a finite energy window. We construct it from the classical action via Monte Carlo with importance sampling. The MC Hamiltonian yields the energy spectrum and corresponding wave functions in a low energy window. This allows to compute thermodynamical o...

UNLABELLED Cardiac SPECT images are degraded by photons scattered in the thorax. Accurate correction for scatter is complicated by the nonuniform density and varied sizes of thoraxes and by the additional attenuation and scatter caused by female patients' breasts. Monte Carlo simulation is a general and accurate method for detailed modeling of scatter. Hence, for 99mTc we compared statistical r...

one of known methods for measuring, forecasting and managing risk is value at risk, which recently has been used by financial institutions extensively. value at risk (var) is a method for recognizing and evaluating risk and uses standard statistical techniques that have daily using in other contexts. this research is seeking a career for managing investment risk in stock exchange and selection ...

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...

In positron emission tomography (PET), photon scattering inside the body causes significant blurring and quantification error in the reconstructed images. To solve this problem we have developed Monte Carlo (MC) based 3D PET reconstruction algorithms implemented on the Graphics Processing Unit (GPU). Our implementation takes multiple Compton scattering into account without any significant addit...

Recently, we have proposed an efficient scheme for Monte Carlo simulations, the multiple "time step" Monte Carlo (MTS-MC) [J. Chem. Phys. 117, 8203 (2002)] based on the separation of the potential interactions into two additive parts. In this paper, the structural and thermodynamic properties of the simple point charge water model combined with the Ewald sum are compared for the MTS-MC real-/re...

The binary Monte Carlo (MC) collision algorithm is a standard and robust method to include Coulomb effects in particle-in-cell (PIC) simulations of plasmas. Here we show that the coupling between PIC MC algorithms can give rise (nonphysical) numerical heating system significantly exceeds observed when these operate independently. We argue this deleterious effect results from an inconsistency pa...

A key feature of modern broadband networks is their ability to provide stringent Quality of Service (QoS) guarantees to different classes of users. A primary QoS parameter is the Cell Loss Probability (CLP), whose typical values can be very small and therefore difficult to estimate through standard Monte Carlo (MC) simulation, since long run times are required to achieve accurate results. In th...

  Introduction: Monte Carlo (MC) is the most common method for simulating virtual SPECT projections. It is useful for optimizing procedures, evaluating correction algorithms and more recently image reconstruction as a forward projector in iterative algorithms; however, the main drawback of MC is its long run time. We introduced a model based method considering the eff...