## Abstract

We propose a new iterative distributed estimation algorithm for Gaussian hidden Markov graphical models with loops. We decompose a loopy graph into a number of linked embedded triangles and then apply a parallel block-Jacobi iteration comprising local linear minimum mean-square-error estimation on each triangle (involving a simple 3x3 matrix inverse computation) followed by an information exchange between neighboring nodes and triangles. A simulation study demonstrates that the algorithm converges extremely rapidly, outperforming a number of existing algorithms. Embedded triangles are simple, local, scalable, fault-tolerant, and energy-efficient, and thus ideally suited for wireless sensor networks.

Original language | English (US) |
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Title of host publication | IEEE Workshop on Statistical Signal Processing Proceedings |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 371-374 |

Number of pages | 4 |

Volume | 2003-January |

ISBN (Print) | 0780379977 |

DOIs | |

State | Published - 2003 |

Event | IEEE Workshop on Statistical Signal Processing, SSP 2003 - St. Louis, United States Duration: Sep 28 2003 → Oct 1 2003 |

### Other

Other | IEEE Workshop on Statistical Signal Processing, SSP 2003 |
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Country | United States |

City | St. Louis |

Period | 9/28/03 → 10/1/03 |

## Keywords

- Computational modeling
- Concurrent computing
- Embedded computing
- Energy efficiency
- Fault tolerance
- Graphical models
- Hidden Markov models
- Iterative algorithms
- Matrix decomposition
- Wireless sensor networks

## ASJC Scopus subject areas

- Electrical and Electronic Engineering
- Applied Mathematics
- Signal Processing
- Computer Science Applications