Textbook in PDF format

This book contains chapters on stochastic processes in both theory and practice in wide-ranging contextual settings. Most chapters describe details of stochastic models that are useful to understand the application of mathematical theories that make us understand the complexities in-depth and also would enrich the advancement of theories themselves. Readers are encouraged to pursue the references contained in the chapters they are interested in for further study. The authors are leading mathematicians and scientists in their own specialties. What emanates in this book is the nature of complex systems themselves, which are described using various aspects of stochastic processes. The topic covers applications of stochastic ordinary and partial differential equations to Markov processes and variations within these areas.
Stochastic Processes – Theoretical Advances and Applications in Complex Systems
Numerical Methods in Python for Solving Systems of Stochastic Differential Equations: An Application to the SIRD Model in Latin America
Numerical Solutions for Modelling Complex Systems with Stochastic Differential and Partial Differential Equations (SDEs/SPDEs)
Some Remarks on Vector Markov Chains and Their Applications to the Description of Many-Particle Systems
Brownian Computing and Its Similarities with Our Brains
Martingales and Partial Differential Equations Price Valuation Models for European Put Options
Optimal Stochastic H∞ Controller Synthesis via LQR Theory