the risk of a nuclear power plant failure. Monte Carlo methods are often implemented using computer simulations, and they can provide approximate solutions...

In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution. Given a probability distribution...

Monte Carlo methods are used in corporate finance and mathematical finance to value and analyze (complex) instruments, portfolios and investments by simulating...

Direct simulation Monte Carlo (DSMC) method uses probabilistic Monte Carlo simulation to solve the Boltzmann equation for finite Knudsen number fluid flows...

In mathematical finance, a Monte Carlo option model uses Monte Carlo methods to calculate the value of an option with multiple sources of uncertainty...

Monte Carlo simulations can be made arbitrarily accurate by increasing the number of photons traced. For example, see the movie, where a Monte Carlo simulation...

therefore no alternative to Monte Carlo simulation. In order to be able to carry out a meaningful Monte Carlo simulation, it is necessary to quantify...

The kinetic Monte Carlo (KMC) method is a Monte Carlo method computer simulation intended to simulate the time evolution of some processes occurring in...

for example in Brigo and Mercurio (2001). The efficient and exact Monte-Carlo simulation of the Hull–White model with time dependent parameters can be easily...

theory of Monte Carlo methods, variance reduction is a procedure used to increase the precision of the estimates obtained for a given simulation or computational...

transpose, which is useful for efficient numerical solutions, e.g., Monte Carlo simulations. It was discovered by André-Louis Cholesky for real matrices, and...

Rohilla Shalizi, Monte Carlo, and Other Kinds of Stochastic Simulation, [online] available at http://bactra.org/notebooks/monte-carlo.html Donald E. Knuth...

Quantum Monte Carlo encompasses a large family of computational methods whose common aim is the study of complex quantum systems. One of the major goals...

the log-returns of the risk factors are multivariate normal. Monte Carlo algorithm simulation generates random market scenarios drawn from that multivariate...

the Metropolis Monte Carlo simulation to molecular systems. It is therefore also a particular subset of the more general Monte Carlo method in statistical...

In computer science, Monte Carlo tree search (MCTS) is a heuristic search algorithm for some kinds of decision processes, most notably those employed...

Monte Carlo in statistical physics refers to the application of the Monte Carlo method to problems in statistical physics, or statistical mechanics. The...

Pritchard et al. used approximation of the posterior distribution by Monte Carlo simulation. Alternative proposal of inference techniques include Gibbs sampling...

methodology is an extension of quantitative project risk analysis with Monte Carlo simulations. It is the next advance beyond critical path method and critical...

biological tissue can be equivalently modeled numerically with Monte Carlo simulations or analytically by the radiative transfer equation (RTE). However...

regular Monte Carlo method or Monte Carlo integration, which are based on sequences of pseudorandom numbers. Monte Carlo and quasi-Monte Carlo methods...

configurations in the Monte-Carlo simulation imposes the use of Euclidean time, by a Wick rotation of spacetime. In lattice Monte-Carlo simulations the aim is to...

Summer 1998) Al-Sharif L, Abdel Aal O.F, Abu Alqumsan A.M The Use Of Monte Carlo Simulation To Evaluate The Passenger Average Travelling Time Under Up-Peak...

Importance sampling is a Monte Carlo method for evaluating properties of a particular distribution, while only having samples generated from a different...

Biology Monte Carlo methods (BioMOCA) have been developed at the University of Illinois at Urbana-Champaign to simulate ion transport in an electrolyte...

statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a probability...

a simulation method aimed at improving the dynamic properties of Monte Carlo method simulations of physical systems, and of Markov chain Monte Carlo (MCMC)...

Monte Carlo (MLMC) methods in numerical analysis are algorithms for computing expectations that arise in stochastic simulations. Just as Monte Carlo methods...

stochastic differential equations. Langevin dynamics simulations are a kind of Monte Carlo simulation. A real world molecular system is unlikely to be present...

Pearson in 1905. Realizations of random walks can be obtained by Monte Carlo simulation. A popular random walk model is that of a random walk on a regular...