## Mathematical model techniques for learning theories |

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### Contents

A Theorys Theorems and a Models Equations | 5 |

Chapter Two PROBABILITY THEORY AND | 11 |

Joint Events | 17 |

Copyright | |

23 other sections not shown

### Common terms and phrases

algebra analogous assume assumptions asymptote Axiom Bernoulli trials binomial distribution Bower Bower's theory Chapter chi-square classical conditioning column complete equation component conditional probabilities constant correct response determinant diagonal difference equations empirical equal equivalent example expected number expected value experimental fundamental matrix given gives guessing correctly implied joint event last error Markov chain mathematical induction mathematical model matrix in Eq multiplied nonconditioned number of errors number of successes number of trials occur operator model outcomes P(Aln P(Gn paired-associate paradigm parameter values particular possible predictions prob probability distribution probability of going probability of response random variable values recursive equation recursive form reinforcement restate Eq root sample space sequence solution solve statement step stochastic matrix summation technique symbolize Table term Theios theoretical total number transition matrix transition probabilities trial n triangular matrix variance vector verbal model yields zero