Author: Jean-Paul Fox
Edition: 2010
Binding: Paperback
ISBN: 1461426065
Edition: 2010
Binding: Paperback
ISBN: 1461426065
Bayesian Item Response Modeling: Theory and Applications (Statistics for Social and Behavioral Sciences)
The purpose of the book is to serve as a handbook for measurement specialists as well as to serve as a textbook for students in classes on the Bayesian approach to modern test theory. Get Bayesian Item Response Modeling diet books 2013 for free.
The book will also serve as a guide to the authors' computer software implementations that will be made available via a website. This software supports the computation of the various models described in the book and can support readers in their own developmental work in IRT using advanced Bayesian techniques/models. The software implementations will be made available in the popular statistical packages Splus and R. The implemented new functions are built on Fortran code that is linked to the Splus and R environments. This approach leads to considerable reduction Check Bayesian Item Response Modeling our best diet books for 2013. All books are available in pdf format and downloadable from rapidshare, 4shared, and mediafire.
Bayesian Item Response Modeling Free
The book will also serve as a guide to the authors' computer software implementations that will be made available via a website. This software supports the computation of the various models described in the book and can support readers in their own developmental work in IRT using advanced Bayesian techniques/models. The software implementations will be made available in the popular statistical packages Splus and R. The implemented new functions are built on Fortran code that is linked to the Splus and R environments he book will also serve as a guide to the authors' computer software implementations that will be made available via a website. This software supports the computation of the various models described in the book and can support readers in their own developmental work in IRT using advanced Bayesian techniques/models. The software implementations will be made available in the popular statistical packages Splus and R. The implemented new functions are built on Fortran code that is linked to the Splus and R environments. This approach leads to considerable reduction
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