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Les strategies informatiques de simulation sont essentielles au statisticien. Afin que celui-ci puisse les utiliser en vue de résoudre des problèmes statistiques, il lui faut au préalable développer son intuition et sa capacité à produire lui-même des modèles de simulation. Ce livre adopte donc le position de vue du programmeur pour exposer ces outils fondamentaux de simulation stochastique. Il montre comment les implémenter sous R et donne les clés d'une meilleure compréhension des méthodes exposées en vue de leur comparaison, sans s'attarder trop longuement sur leur justification théorique. Les auteurs présentent les algorithmes de base pour la génération de données aléatoires, les tactics de Monte-Carlo pour l'intégration et l'optimisation, les diagnostics de convergence, les chaînes de Markov, les algorithmes adaptatifs, les algorithmes de Metropolis- Hastings et de Gibbs.
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