توضیحات
Monte Carlo with Python (2024) offers a complete introduction to Monte Carlo simulation techniques—a fundamental approach in scientific computing, finance, and data science. The book explains the mathematical principles of random sampling, statistical estimation, and stochastic modeling, and then translates them into practical Python implementations. Key topics include random number generation, integration, Bayesian inference, risk modeling, Markov Chain Monte Carlo (MCMC), and variance reduction techniques. With step-by-step code examples, visualization tools, and applied case studies, this book helps readers understand how Monte Carlo algorithms are used for prediction, optimization, and decision-making under uncertainty. It is ideal for data scientists, quantitative analysts, and computational researchers seeking to apply probabilistic models using Python.
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