# Math for Modelers.

Review Title |
Math for Modelers. |

Year |
1973 |

Month |
Mar |

Abstract |
Reviews the book, Mathematical Model Techniques for Learning Theories by Gustav Levine, C. J. Burke (see record 1973-06011-000). The Preface of this book would lead us to believe that the author's main concern is the construction of mathematical learning models, and, indeed, a conscientious reader is likely to develop skills that will assist him in formulating models. Most of the techniques referred to in the title are best viewed as methods of deriving predictions from previously formulated models. The mathematical methods developed in later chapters are probability, summation of geometric series, solution of difference equations, matrix algebra, and Markov chains. The material on matrix algebra is quite adequate and is not duplicated in other books on learning models. Computer-based methods, such as Monte Carlo simulation, and approximation techniques, like the expected operator approximations of Bush and Mosteller, are not mentioned. This is most regrettable because only the very simplest models permit derivation of exact formulas for predictions, while simulation and approximation have much broader applicability. (PsycINFO Database Record (c) 2017 APA, all rights reserved) |

Serial Title |
Contemporary Psychology |

Volume/Issue |
Vol 18(3) |

Publisher |
American Psychological Association |

APA Database |
PsycCritiques |

Document Type |
Review-Book |

File Name |
2006-06248-003.pdf |

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