Simulation Techniques provides a comprehensive introduction to the theory, methodology and practical implementation of simulation techniques, with particular emphasis on statistical and agricultural applications. The book begins with the fundamental concepts of simulation, classification of models, system components, applications, advantages and limitations, and the systematic steps involved in conducting a simulation study.A substantial part of the book is devoted to probability distributions, covering both discrete and continuous distributions. The discrete distributions include Bernoulli, Binomial, Geometric, Negative Binomial, Hypergeometric and Poisson distributions, while the continuous distributions include Uniform, Triangular, Normal, Lognormal, Exponential, Erlang, Gamma and Weibull distributions. Their mathematical properties and agricultural examples are presented throughout the chapter.The book subsequently explains the implementation of simulation methods for probability distributions and introduces practical computational approaches. Agricultural examples include temperature, crop yield, rainfall, pest counts, seed germination, farm income and fertilizer use.

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