## Response models for detection of changeThe optimal policy; A response model with a fixed probability boundary; A response model with a fixed number of observations; A response model with a fixed number of successive observations; Sensitivity analysis; Multi-state detection of change; Experimental research; Extensions. |

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### Contents

The optimal policy | 16 |

A response model with a fixed | 32 |

A response model with a fixed number | 64 |

Copyright | |

7 other sections not shown

### Other editions - View all

Response Models for Detection of Change Anatol Rapoport,W Stein,G Burkheimer No preview available - 2014 |

### Common terms and phrases

arbitrary assumed assumptions Burkheimer change from S0 change problems change processes Chapter computed considered decision behavior decision problem decision rule Decision Theory defined delay loss denote the number density functions detection of change DM's error loss expected number experimental experiments false alarm Figure FNOB and FNSOB geometric distribution given incorrect Stop decision increase in expected infinite-horizon learning loss functions loss structure Luce and Green Markov chain minimum expected loss model FNOB model FPB MTDC number of observations observations taken obtained occurred optimal policy parameters Poisson distribution presented prior probability probability distribution probability of change probability vector problem TDC random variable Rapoport recursive equation response models RN(P signal detectability SOC curves SOC points solution stage starts in S0 STDC sufficient statistic Table TDC and DC TDC or DC terminates tion total expected loss total number transition matrix trial of change two-state vector