dorsal/arxiv
View SchemaProbabilistic methods for data fusion
| Authors | A. Mohammad-Djafari |
|---|---|
| Categories | |
| ArXiv ID | physics/0111118 |
| URL | https://arxiv.org/abs/physics/0111118 |
Abstract
The main object of this paper is to show how we can use classical probabilistic methods such as Maximum Entropy (ME), maximum likelihood (ML) and/or Bayesian (BAYES) approaches to do microscopic and macroscopic data fusion. Actually ME can be used to assign a probability law to an unknown quantity when we have macroscopic data (expectations) on it. ML can be used to estimate the parameters of a probability law when we have microscopic data (direct observation). BAYES can be used to update a prior probability law when we have microscopic data through the likelihood. When we have both microscopic and macroscopic data we can use first ME to assign a prior and then use BAYES to update it to the posterior law thus doing the desired data fusion. However, in practical data fusion applications, we may still need some engineering feeling to propose realistic data fusion solutions. Some simple examples in sensor data fusion and image reconstruction using different kind of data are presented to illustrate these ideas. Keywords: Data fusion, Maximum entropy, Maximum likelihood, Bayesian data fusion, EM algorithm.
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"abstract": "The main object of this paper is to show how we can use classical\nprobabilistic methods such as Maximum Entropy (ME), maximum likelihood (ML)\nand/or Bayesian (BAYES) approaches to do microscopic and macroscopic data\nfusion. Actually ME can be used to assign a probability law to an unknown\nquantity when we have macroscopic data (expectations) on it. ML can be used to\nestimate the parameters of a probability law when we have microscopic data\n(direct observation). BAYES can be used to update a prior probability law when\nwe have microscopic data through the likelihood. When we have both microscopic\nand macroscopic data we can use first ME to assign a prior and then use BAYES\nto update it to the posterior law thus doing the desired data fusion. However,\nin practical data fusion applications, we may still need some engineering\nfeeling to propose realistic data fusion solutions. Some simple examples in\nsensor data fusion and image reconstruction using different kind of data are\npresented to illustrate these ideas. Keywords: Data fusion, Maximum entropy,\nMaximum likelihood, Bayesian data fusion, EM algorithm.",
"arxiv_id": "physics/0111118",
"authors": [
"A. Mohammad-Djafari"
],
"categories": [
"physics.data-an"
],
"title": "Probabilistic methods for data fusion",
"url": "https://arxiv.org/abs/physics/0111118"
},
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"execution_id": "73305f3a-6945-4920-be0b-2eaecbb553e0",
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