Simulation in Statistics
Christian P. Robert (University Paris-Dauphine, IUF, and CREST)
TL;DR
This paper reviews simulation tools in statistics, focusing on recent advances in adaptive MCMC and ABC algorithms, highlighting their importance for analyzing complex probabilistic models.
Contribution
It provides a comprehensive overview of simulation methods tailored for statistical challenges, emphasizing recent methodological developments.
Findings
Adaptive MCMC algorithms improve sampling efficiency.
Approximate Bayesian computation enables analysis of intractable models.
Simulation tools are essential for complex probabilistic models.
Abstract
Simulation has become a standard tool in statistics because it may be the only tool available for analysing 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.
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