Monte Carlo methods have become indispensable tools in contemporary statistical analysis and computational mathematics. This book offers a thorough introduction to these techniques using the R programming language, making complex probabilistic and statistical computations accessible to practitioners and students alike. By blending theoretical foundations with practical implementations, the authors provide readers with a robust understanding of stochastic simulation and its applications.

• Foundational Concepts: The text starts by establishing the theoretical underpinnings of Monte Carlo methods, including probability distributions, random number generation, and convergence properties.
• Simulation Techniques: It covers key algorithms such as inversion,...
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