The EM algorithm is a popular tool for maximizing likelihood functions in the presence of missing data. Unfortunately, EM often requires the evaluation of analytically intractable and high-dimensional integrals. The Monte Carlo EM (MCEM) algorithm is the natural extension of EM that employs Monte Carlo methods to estimate the relevant integrals. Typically, a very large Monte Carlo sample size i...

The expectation–maximization (EM) algorithm is a popular tool for maximizing likelihood functions in the presence of missing data. Unfortunately, EM often requires the evaluation of analytically intractable and high dimensional integrals. The Monte Carlo EM (MCEM) algorithm is the natural extension of EM that employs Monte Carlo methods to estimate the relevant integrals.Typically, a very large...

In this article we develop a collection of results associated to the analysis of the Sequential Monte Carlo (SMC) samplers algorithm, in the context of high-dimensional i.i.d. target probabilities. The SMC samplers algorithm can be designed to sample from a single probability distribution, using Monte Carlo to approximate expectations w.r.t. this law. Given a target density in d−dimensions our ...

BACKGROUND The accuracy of dose calculation is crucial to the quality of treatment planning and, consequently, to the dose delivered to patients undergoing radiation therapy. Current general calculation algorithms such as Pencil Beam Convolution (PBC) and Collapsed Cone Convolution (CCC) have shortcomings in regard to severe inhomogeneities, particularly in those regions where charged particle ...

For the problem that the traditional static positioning algorithms can not locate the mobile network node accurately, and the traditional Monte Carlo localization algorithm features low positioning accuracy and poor positioning accuracy due to the low sampling efficiency of the nodes, an improved Monte Carlo (Higher Monte Carlo, HMCL) localization algorithm is thereby proposed. The RSSI distanc...

1 Las Vegas and Monte Carlo algorithms Definition 1.1 A Las Vegas algorithm!Las Vegas algorithm is a randomized algorithms that always return the correct result. The only variant is that it's running time might change between executions. An example for a Las Vegas algorithm is the QuickSort algorithm. Definition 1.2 Monte Carlo algorithm!Monte Carlo algorithm is a randomized algorithm that migh...

This paper presents importance sampling Monte-Carlo algorithms for the calculation of belief functions combination. When the connict between the evidence is not very high a simple Monte-Carlo algorithm can produce good quality estimations. For the case of highly connicting evidences a Markov chain Monte-Carlo algorithm was also proposed. In this paper, a new class of importance sampling based a...

Stochastic approximation Monte Carlo (SAMC) has recently been proposed by Liang, Liu and Carroll [J. Amer. Statist. Assoc. 102 (2007) 305–320] as a general simulation and optimization algorithm. In this paper, we propose to improve its convergence using smoothing methods and discuss the application of the new algorithm to Bayesian model selection problems. The new algorithm is tested through a ...

Stochastic approximation Monte Carlo (SAMC) has recently been proposed by Liang, Liu and Carroll (2007) as a general simulation and optimization algorithm. In this paper, we propose to improve its convergence using smoothing methods and discuss the application of the new algorithm to Bayesian model selection problems. The new algorithm is tested through a change-point identification example. Th...