Stochastic Quasi-Newton Langevin Monte Carlo

This approach can be generalized to obtain a continuous CDF estimator and then an unbiased density estimator, via the likelihood ratio (LR) simulation-based derivative estimation

Randomized quasi-Monte Carlo (RQMC) methods replace the independent random numbers by dependent vectors of uniform random numbers that cover the space more evenly.. When estimating

Monte Carlo MC simulation is widely used to estimate the expectation E[X ] of a random variable X and compute a con dence interval on E[X ]... What this talk

Quasi-Monte Carlo QMC and randomized QMC RQMC methods have been studied extensively for estimating an integral, say E[X ], and for approximating a function from its evaluation at

Randomized Quasi-Monte Carlo: Theory, Choice of Discrepancy, and Applications featuring randomly-shifted lattice rules Pierre L’Ecuyer and David Munger.. Informatique et

This paper investigates poor behavior of Markov chain Monte Carlo (MCMC) procedures which provides good information for construction of efficient MCMC procedures.. There have a

To tackle large dimensions, other methods have been devised that are based on a non-independent sampling: Markov chain Monte Carlo methods (MCMC).. The prototype of MCMC methods is

It is of course possible to generalize the little example from a probability λ = 1/6 to a general value of λ, and from a single spin to a general statistical mechanics problem