Sequential Monte Carlo (SMC) samplers are an alternative to MCMC for Bayesian computation. However, their performance depends strongly on the Markov kernels used rejuvenate particles. We discuss how calibrate automatically (using current particles) Hamiltonian within SMC. To do so, we build upon adaptive SMC approach of Fearnhead and Taylor (2013), also suggest methods. illustrate advantages us...

introduction: in recent decades, several monte carlo codes have been introduced for research and medical applications. these methods provide both accurate and detailed calculation of particle transport from linear accelerators. the main drawback of monte carlo techniques is the extremely long computing time that is required in order to obtain a dose distribution with good statistical accuracy. ...

medical linear accelerators are one of the most widespread methods for cancer treatment. despite their advantages, unwanted photoneutrons are produced by high energy linacs. this photoneutrons are as undesired doses to patients and a significant problem for radiation protection of the staffs and patients. photoneutrons radiological risk must be evaluated because of their high let and range.in o...

We consider the classical risk model with subexponential claim size distribution. Three methods are presented to simulate the probability of ultimate ruin and we investigate their asymptotic efficiency. One, based upon a conditional Monte Carlo idea involving the order statistics, is shown to be asymptotically efficient in a certain sense. We use the simulation methods to study the accuracy of ...

In these notes we describe the general Monte-Carlo framework for estimating expectations. We also give several applications from finance and describe how to simulate correlated normal random variables using the Cholesky decomposition of the covariance matrix. The ability to generate correlated normal random variables finds applications throughout finance. These applications include simulating c...

Default probability is a fundamental variable determining the credit worthiness of a firm and equity volatility estimation plays a key role in its evaluation. Assuming a structural credit risk modeling approach, we study the impact of choosing different non parametric equity volatility estimators on default probability evaluation, when market microstructure noise is considered. A general stocha...

this paper proposes a hybrid method to find cumulative distribution function (cdf) of completion time of gert-type networks (gtn) which have no loop and have only exclusive-or nodes. proposed method is cre-ated by combining an analytical transformation with gaussian quadrature formula. also the combined crude monte carlo simulation and combined conditional monte carlo simulation are developed a...

Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N~^), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods and variance reduction techniques. Accelerated convergence for Monte Ca...

This paper surveys recent research on using Monte Carlo techniques to improve quasi-Monte Carlo techniques. Randomized quasi-Monte Carlo methods provide a basis for error estimation. They have, in the special case of scrambled nets, also been observed to improve accuracy. Finally through Latin supercube sampling it is possible to use Monte Carlo methods to extend quasi-Monte Carlo methods to hi...