M. Necati Özisik (1923-2008) retired in 1998 as Professor Emeritus of North Carolina State University's Mechanical and Aerospace Engineering Department, where he spent most of his academic career. Professor Özisik dedicated his life to education and research in heat transfer. His outstanding contributions earned him numerous awards, including the Outstanding Engineering Educator Award from the American Society for Engineering Education in 1992. He authored eleven books, many of which were published in different languages.

Helcio R. B. Orlande earned a BS in mechanical engineering at the Federal University of Rio de Janeiro (UFRJ) in 1987 and an MS in mechanical engineering from the same university in 1989. After earning a PhD in mechanical engineering in 1993 at North Carolina State University, he joined the Department of Mechanical Engineering of UFRJ, where he was the Department Head from 2006 to 2007. His research areas of interest include the solution of inverse heat and mass transfer problems as well as the use of numerical, analytical and hybrid numerical-analytical methods of solution of direct heat and mass transfer problems. He is a member of the Scientific Council of the International Centre for Heat and Mass Transfer and a Delegate in the Assembly for International Heat Transfer Conferences. He serves as an Associate Editor for the journals Heat Transfer Engineering, Inverse Problems in Science and Engineering, High Temperatures - High Pressures and International Journal of Thermal Sciences.

Part I: Introduction and Parameter Estimation 1. Basic Concepts 2. Parameter Estimation: Minimization of an Objective Function without Prior Information about the Unknown Parameters 3. Parameter Estimation: Minimization of an Objective Function with Prior Information about the Unknown Parameters 4. Parameter Estimation: Stochastic Simulation with Prior Information about the Unknown Parameters Part II: Function Estimation 5. Function Estimation: Minimization of an Objective Functional without Prior Information about the Unknown Functions 6. Function Estimation: Solution within the Bayesian Framework of Statistics with Prior Information about the Unknown Functions Part III: State Estimation 7. State Estimation: Kalman Filter 8. State Estimation: Particle Filter