The CV-S method is a high-efficiency random sampling to estimate statistical moments of variables, and it uses an approximated target parameter which are linearly dependent on input as mockup parameter. In order enhance the applicability method, we propose use different from but similar whose sensitivity coefficients available. present work, nuclear fuel burnup problems concerned, standard devi...

The waste-recycling Monte Carlo (WR) algorithm introduced by physicists is a modification of the (multi-proposal) Metropolis-Hastings algorithm, which makes use of all the proposals in the empirical mean, whereas the standard (multi-proposal) Metropolis-Hastings algorithm only uses the accepted proposals. In this paper, we extend the WR algorithm into a general control variate technique and exh...

Deep neural networks are powerful parametric models that can be trained efficiently using the backpropagation algorithm. Stochastic neural networks combine the power of large parametric functions with that of graphical models, which makes it possible to learn very complex distributions. However, as backpropagation is not directly applicable to stochastic networks that include discrete sampling ...

Monte Carlo algorithms typically need to generate random variates from a probability distribution described by an unnormalized density or probability mass function. Perfect simulation algorithms generate random variates exactly from these distributions, but have a running time T that is itself an unbounded random variable. This article shows that commonly used protocols for creating perfect sim...

An expression is derived for the distribution of a mixture of real and complex normal variates.

This paper proposes methods to improve Monte Carlo estimates when the Independent MetropolisHastings Algorithm (IMHA) is used. Our rst approach uses a control variate based on the sample generated by the proposal distribution. We derive the variance of our estimator for a xed sample size n and show that, as n tends to in nity, this variance is asymptotically smaller than the one obtained with t...

The sample mean is one of the most natural estimators of the population mean based on independent identically distributed sample. However, if some control variate is available, it is known that the control variate method reduces the variance of the sample mean. The control variate method often assumes that the variable of intersest and the control variable are i.i.d. Here we assume that these v...

Montecarlo methods can be used to price derivatives for which closed evaluation formulas are not available or di cult to derive. A drawback of the method can be its high computational cost, especially if applied to basket options, whose payo s depend on more than one asset. This article presents two kinds of control variates to reduce variance of estimates, based on unconditional and conditiona...

This paper shows how to decompose the dollar profit earned from an option into two basic components: ¶ mispricing of the option relative to the asset at the time of purchase, and · profit from subsequent fortuitous changes or mispricing of the underlying asset. This separation hinges on measuring the “true relative value” of the option from its realized payoff. The payoff from any one option ha...