A simple sequential stopping rule for Monte Carlo Simulation

Investigating to Find the Stopping Rule for Monte Carlo Computations

In this paper introduces a rule to drop the Monte Carlo computation when the accuracy of the Monte Carlo computation reaches to the amount of error which is planned in the beginning of the computation.

Stopping-time resampling for sequential Monte Carlo methods

Motivated by the statistical inference problem in population genetics, we present a new sequential importance sampling with resampling strategy. The idea of resampling is key to the recent surge of popularity of sequential Monte Carlo methods in the statistics and engineering communities, but existing resampling techniques do not work well for coalescent-based inference problems in population g...

A New Approach for Monte Carlo Simulation of RAFT Polymerization

In this work, based on experimental observations and exact theoretical predictions, the kinetic scheme of RAFT polymerization is extended to a wider range of reactions such as irreversible intermediate radical terminations and reversible transfer reactions. The reactions which have been labeled as kinetic scheme are the more probable existing reactions as the theoretical point of view. The ...

Markov Chain Monte Carlo Simulation Made Simple

Sequential Monte Carlo Samplers

In this paper, we propose a methodology to sample sequentially from a sequence of probability distributions known up to a normalizing constant and defined on a common space. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time using Sequential Monte Carlo methods. This methodology allows us to derive simple algorithms to make para...