• Summary
  • Contents
  • Subject index

An accessible introduction to the principles of computational and mathematical modeling in psychology and cognitive science

This practical and readable work provides students and researchers, who are new to cognitive modeling, with the background and core knowledge they need to interpret published reports, and develop and apply models of their own. The book is structured to help readers understand the logic of individual component techniques and their relationships to each other.

Basic Parameter Estimation Techniques
Basic parameter estimation techniques

There is little doubt that even before you started reading this book, you had already fit many models to data. No one who has completed an introductory statistics course can escape learning about linear regression. It turns out that every time you computed a regression line, you were actually fitting a model—namely, the regression line with its two parameters, slope and intercept—to the data.

3.1 Fitting Models to Data: Parameter Estimation

For familiarity's sake, we therefore initiate our discussion of parameter estimation within the linear regression framework. Specifically, we begin by defining the “model” yi = b0+ b1xi + ei, which expresses each observation yi as a function of the measurement of the independent variable xi and two to-be-estimated parameters ...

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