Stochastic Modeling and Statistical Methods

Stochastic Modeling and Statistical Methods by Ioannis S. Triantafyllou, Sonia Malefaki, and Alex Karagrigoriou delivers a clear, modern bridge between probability theory and practical data-driven decision-making. Whether you’re a graduate student in Athens, a data scientist in London, or a researcher in North America, this book equips you to model uncertainty confidently and extract reliable inferences from complex data.

Inside, the authors combine rigorous theory with accessible exposition to cover essential topics such as stochastic processes, Markov chains, time series, Monte Carlo methods, and advanced statistical inference. Each chapter builds intuition through worked examples, step-by-step derivations, and applied scenarios—making abstract concepts tangible for engineers, economists, actuaries, and applied statisticians.

The strength of this volume lies in its balance: formal foundations that support robust modeling, paired with practical techniques you can adapt to real-world problems in finance, telecommunications, environmental science, and public health. Emphasis on computational approaches and model validation ensures you can implement and test methods on regional and global datasets alike.

If you need a dependable reference to sharpen your analytical toolkit or a textbook that supports graduate coursework and professional development, this book is an excellent choice. Precise, engaging, and relevant to contemporary research environments across Europe and beyond, Stochastic Modeling and Statistical Methods is designed to advance both understanding and application.

Order your copy today to strengthen your modeling skills and bring clarity to uncertainty in your next project.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.