### Abstract

Original language | English |
---|---|

Title of host publication | Maximum Entropy and Bayesian Methods |

Editors | Gary J. Erickson, Joshua T. Rychert, C. Ray Smith |

Publisher | Springer Netherlands |

Pages | 87-99 |

Number of pages | 13 |

ISBN (Print) | 978-94-010-6111-7 978-94-011-5028-6 |

Publication status | Published - 1998 |

### Publication series

Name | Fundamental Theories of Physics |
---|---|

Publisher | Springer Netherlands |

### Fingerprint

### Keywords

- Artificial Intelligence (incl. Robotics), Auto-classification, auto-clustering, Coding and Information Theory, Discrete Mathematics in Computer Science, group analysis, Mahalonobis distance, Probability Theory and Stochastic Processes, Statistics, general

### Cite this

*Maximum Entropy and Bayesian Methods*(pp. 87-99). (Fundamental Theories of Physics). Springer Netherlands.

**Bayesian Group Analysis.** / Linden, W. Von Der; Dose, V.; Ramaswami, A.

Research output: Chapter in Book/Report/Conference proceeding › Other chapter contribution › Research

*Maximum Entropy and Bayesian Methods.*Fundamental Theories of Physics, Springer Netherlands, pp. 87-99.

}

TY - CHAP

T1 - Bayesian Group Analysis

AU - Linden, W. Von Der

AU - Dose, V.

AU - Ramaswami, A.

N1 - DOI: 10.1007/978-94-011-5028-67

PY - 1998

Y1 - 1998

N2 - In many fields of research the following problem is encountered: a large collection of data is given for which a detailed theory is yet missing. To gain insight into the underlying problem it is important to reveal the interrelationships in the data and to determine the relevant input and response quantities. A central part of this task is to find the natural splitting of the data into groups and to analyze the respective characteristics. Bayesian probability theory is invoked for a consistent treatment of these problems. Due to Ockham’s Razor, which is an integral part of the theory, the simplest group configuration that still fits the data has the highest probability. In addition the Bayesian approach allows to eliminate outliers, which otherwise could lead to erroneous conclusions. Simple textbook and mock data sets are analyzed in order to assess the Bayesian approach.

AB - In many fields of research the following problem is encountered: a large collection of data is given for which a detailed theory is yet missing. To gain insight into the underlying problem it is important to reveal the interrelationships in the data and to determine the relevant input and response quantities. A central part of this task is to find the natural splitting of the data into groups and to analyze the respective characteristics. Bayesian probability theory is invoked for a consistent treatment of these problems. Due to Ockham’s Razor, which is an integral part of the theory, the simplest group configuration that still fits the data has the highest probability. In addition the Bayesian approach allows to eliminate outliers, which otherwise could lead to erroneous conclusions. Simple textbook and mock data sets are analyzed in order to assess the Bayesian approach.

KW - Artificial Intelligence (incl. Robotics), Auto-classification, auto-clustering, Coding and Information Theory, Discrete Mathematics in Computer Science, group analysis, Mahalonobis distance, Probability Theory and Stochastic Processes, Statistics, general

M3 - Other chapter contribution

SN - 978-94-010-6111-7 978-94-011-5028-6

T3 - Fundamental Theories of Physics

SP - 87

EP - 99

BT - Maximum Entropy and Bayesian Methods

A2 - Erickson, Gary J.

A2 - Rychert, Joshua T.

A2 - Smith, C. Ray

PB - Springer Netherlands

ER -