Warren E. Stewart, Sc.D. and Michael Caracotsios, Ph.D.
Scientific learning is an iterative process that employs experimentation, mathematical modeling and nonlinear parameter estimation, model criticism and discrimination. The mathematical modeling task encapsulates our knowledge in a well defined set of user postulated functions. Estimation is applied to estimate adjustable parameters and their posterior probability density conditional on the model’s truth. Model criticism and discrimination induces enhancement and further modification.
- Chemical Reaction Models
- Chemical Reactor Models
- Introduction to Probability and Statistics
- Introduction to Bayesian Estimation
- Process Modeling with Single-Response Data
- Process Modeling with Multi-Response Data
- Appendix A: Solution of Linear Algebraic Equations
- Appendix B: DDAPLUS Documentation
- Appendix C: GREGPLUS Documentation