### Abstract

Original language | English |
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Title of host publication | Computational Information Geometry |

Subtitle of host publication | For Image and Signal Processing |

Editors | Frank Nielsen, Kit Dodson, Frank Critchley |

Publisher | Springer |

Pages | 33-62 |

ISBN (Electronic) | 9783319470580 |

ISBN (Print) | 9783319470566 |

DOIs | |

Publication status | Published - 2017 |

### Publication series

Name | Signals and Communication Technology |
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### Fingerprint

### Cite this

*Computational Information Geometry : For Image and Signal Processing*(pp. 33-62). (Signals and Communication Technology). Springer. https://doi.org/10.1007/978-3-319-47058-0_2

**Towards the geometry of model sensitivity : an Illustration.** / Anaya-Izquierdo, Karim; Critchley, Frank; Marriott, Paul; Vos, Paul.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

*Computational Information Geometry : For Image and Signal Processing.*Signals and Communication Technology, Springer, pp. 33-62. https://doi.org/10.1007/978-3-319-47058-0_2

}

TY - CHAP

T1 - Towards the geometry of model sensitivity

T2 - an Illustration

AU - Anaya-Izquierdo, Karim

AU - Critchley, Frank

AU - Marriott, Paul

AU - Vos, Paul

PY - 2017

Y1 - 2017

N2 - In statistical practice model building, sensitivity and uncertainty are major concerns of the analyst. This paper looks at these issues from an information geometric point of view. Here, we define sensitivity to mean understanding how inference about a problem of interest changes with perturbations of the model. In particular it is an example of what we call computational information geometry. The embedding of simple models in much larger information geometric spaces is shown to illuminate these critically important issues.

AB - In statistical practice model building, sensitivity and uncertainty are major concerns of the analyst. This paper looks at these issues from an information geometric point of view. Here, we define sensitivity to mean understanding how inference about a problem of interest changes with perturbations of the model. In particular it is an example of what we call computational information geometry. The embedding of simple models in much larger information geometric spaces is shown to illuminate these critically important issues.

UR - http://dx.doi.org/10.1007/978-3-319-47058-0_2

U2 - 10.1007/978-3-319-47058-0_2

DO - 10.1007/978-3-319-47058-0_2

M3 - Chapter

SN - 9783319470566

T3 - Signals and Communication Technology

SP - 33

EP - 62

BT - Computational Information Geometry

A2 - Nielsen, Frank

A2 - Dodson, Kit

A2 - Critchley, Frank

PB - Springer

ER -