The book is a comprehensive academic resource covering the fundamental concepts, statistical techniques, and computational approaches used in simulation studies. The book introduces the principles of simulation, probability distributions, random number generation, sampling methods, Markov chains, and advanced stochastic simulation techniques.

Beginning with an introduction to simulation models, their classification, purposes, and implementation steps, the book develops the reader’s understanding of probability distributions and their application in discrete and continuous simulation methods. It further explores the generation and testing of random numbers, randomization tests, and resampling techniques, including the jackknife and bootstrap methods.

The book also provides an introduction to Markov chains and advanced Monte Carlo approaches, including Markov Chain Monte Carlo (MCMC), the Hastings–Metropolis algorithm, Gibbs sampling, simulated tempering, reversible jump MCMC, and multi-grid methods. The final chapter extends simulation applications to generalized linear models and time-series models, along with the analysis of simulated datasets using popular computer software packages.

Designed for students, researchers, teachers, and professionals, this book serves as a useful reference for understanding both the theoretical foundations and practical applications of simulation in statistics, data analysis, and computational research.

Key Features:

• Introduces the concepts, classification, and applications of simulation models.
• Covers discrete and continuous probability distributions and their simulation.
• Explains random number generation, testing, and randomization methods.
• Discusses sampling, resampling, jackknife, and bootstrap techniques.
• Introduces Markov chains and stochastic simulation.
• Covers Monte Carlo methods and advanced MCMC algorithms.
• Explores simulation of generalized linear and time-series models.
• Includes analysis of simulated datasets using popular computer software packages.

Dr. Prity Kumari is an alumna of Banaras Hindu University, Varanasi, Uttar Pradesh, India, and has been serving as an Assistant Professor at the College of Horticulture, Anand Agricultural University, Gujarat, since 2015. Her research expertise encompasses time-series forecasting using statistical models and advanced deep-learning and artificial intelligence techniques. She has worked with methodologies such as ARIMA, ARCH/GARCH, artificial neural networks (ANN), machine learning, memory-based machine-learning models, and convolutional neural networks (CNN). Dr. Kumari has published research papers in reputed journals and authored several books and book chapters. She actively mentors and guides master's and Ph.D. students in agriculture. She has also served as a visiting fellow at Western Sydney University, further broadening her research experience.

Darshan L. Kothiya is a Ph.D. scholar in the Department of Agricultural Statistics at Anand Agricultural University, Anand, Gujarat. He completed his B.Sc. (Hons.) in Agriculture from Navsari Agricultural University and his M.Sc. in Agricultural Statistics from Anand Agricultural University, where he is pursuing his doctoral research. He received the Best Thesis Award from the B.A. College of Agriculture Alumni Association in recognition of his outstanding master's research. He has authored eight book chapters published in academic books. His research interests include experimental design, agricultural data analysis, multivariate statistical techniques, machine learning, and data analytics in agriculture. Proficient in R and Python, he applies advanced statistical and computational methods to address agricultural research challenges, with expertise spanning both theoretical and applied agricultural statistics.

Jignesh K. Parmar is a Ph.D. scholar in the Department of Agricultural Statistics at Anand Agricultural University, Anand, Gujarat. He completed his B.Sc. (Hons.) in Agriculture from Navsari Agricultural University and earned his M.Sc. in Agricultural Statistics from Anand Agricultural University, achieving the highest Overall Grade Point Average (OGPA) and receiving a Gold Medal. His research interests include disease classification, simulation techniques, agricultural data analysis, experimental design, and commodity price forecasting. Proficient in R and Python, he applies advanced statistical and computational methods to address complex agricultural research problems. He has also served as a Statistical Expert at AgriAnalyze, contributing to the development of analytical solutions and conducting training programmes on Design of Experiments (DOE) and statistical analysis. His work reflects a strong commitment to data-driven agricultural research and capacity building.

Chapter 1. Introduction, Classification of Models, Uses and Purposes of Simulation, Steps of Simulation

Chapter 2. Review of Probability Distributions

Chapter 3. Implementation of Simulation Methods for Discrete and Continuous Probability Distributions

Chapter 4. Generation and Testing of Random Numbers, Randomization Tests

Chapter 5. Sampling and Resampling Methods, Theory and Application of the Jackknife and the Bootstrap

Chapter 6. Markov Chain

Chapter 7. Stochastic Simulation, Monte Carlo Methods, MCMC, Hastings–Metropolis Algorithm, Gibbs Sampler, Critical Slowing Down and Remedies, Auxiliary Variables, Simulated Tempering, Reversible Jump MCMC, Multi-grid Methods

Chapter 8. Simulation of Generalized Linear Models, Simulation of Time Series Models, Analysis of Simulated Data Sets Using Popular Computer Software Package