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  • 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.

Maximum Likelihood Estimation
Maximum likelihood estimation

In the previous chapters, we encountered one of the key issues in computational modeling: A full, quantitative specification of a model involves not just a description of the model (in the form of algorithms or equations) but also a specification of the parameters of the model and their values. Although in some cases, we can use known parameter values (e.g., those determined from previous applications of the model; see, e.g., Oberauer & Lewandowsky, 2008), in most cases we must estimate those parameters from the data. Chapter 3 described the basics of estimating parameters by varying the parameters to minimize the root mean squared deviation (RMSD) between the data and the model's predictions. Chapter 4 deals with a principled and popular ...

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