Stochastic simulation
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Short communication: AMCMC: An R interface for adaptive MCMC
Computational Statistics & Data Analysis
Adaptive importance sampling in general mixture classes
Statistics and Computing
Inferring population history with DIY ABC
Bioinformatics
Short communication: Grapham: Graphical models with adaptive random walk Metropolis algorithms
Computational Statistics & Data Analysis
Non-linear regression models for Approximate Bayesian Computation
Statistics and Computing
Computational Statistics
Hi-index | 0.00 |
Simulation has become a standard tool in statistics because it may be the only tool available for analyzing some classes of probabilistic models. We review in this paper simulation tools that have been specifically derived to address statistical challenges and, in particular, recent advances in the areas of adaptive Markov chain Monte Carlo (MCMC) algorithms, and approximate Bayesian calculation (ABC) algorithms.