A Bayesian Analysis of the Radioactive Releases of Fukushima

Ryota Tomioka, Morten Mrup
; Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:1243-1251, 2012.

Abstract

The Fukushima Daiichi disaster 11 March, 2011 is considered the largest nuclear accident since the 1986 Chernobyl disaster and has been rated at level 7 on the International Nuclear Event Scale. As different radioactive materials have different effects to human body, it is important to know the types of nuclides and their levels of concentration from the recorded mixture of radiations to well take necessary measures. We presently formulate a Bayesian generative model for the data available on radioactive releases from the Fukushima Daiichi disaster across Japan. The model can infer from the sparsely sampled measurements what nuclides are present as well as their concentration levels. An important property of the proposed model is that it admits unique recovery of the parameters. On synthetic data we demonstrate that our model is able to infer the underlying components and on data from the Fukushima Daiichi plant we establish that the model is able to well account for the data. We further demonstrate how the model extends to include all the available measurements recorded throughout Japan. The model can be considered a first attempt to apply Bayesian learning unsupervised in order to give a more detailed account also of the latent structure present in the data of the Fukushima Daiichi disaster.

Cite this Paper


BibTeX
@InProceedings{pmlr-v22-tomioka12, title = {A Bayesian Analysis of the Radioactive Releases of Fukushima}, author = {Ryota Tomioka and Morten Mrup}, booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics}, pages = {1243--1251}, year = {2012}, editor = {Neil D. Lawrence and Mark Girolami}, volume = {22}, series = {Proceedings of Machine Learning Research}, address = {La Palma, Canary Islands}, month = {21--23 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v22/tomioka12/tomioka12.pdf}, url = {http://proceedings.mlr.press/v22/tomioka12.html}, abstract = {The Fukushima Daiichi disaster 11 March, 2011 is considered the largest nuclear accident since the 1986 Chernobyl disaster and has been rated at level 7 on the International Nuclear Event Scale. As different radioactive materials have different effects to human body, it is important to know the types of nuclides and their levels of concentration from the recorded mixture of radiations to well take necessary measures. We presently formulate a Bayesian generative model for the data available on radioactive releases from the Fukushima Daiichi disaster across Japan. The model can infer from the sparsely sampled measurements what nuclides are present as well as their concentration levels. An important property of the proposed model is that it admits unique recovery of the parameters. On synthetic data we demonstrate that our model is able to infer the underlying components and on data from the Fukushima Daiichi plant we establish that the model is able to well account for the data. We further demonstrate how the model extends to include all the available measurements recorded throughout Japan. The model can be considered a first attempt to apply Bayesian learning unsupervised in order to give a more detailed account also of the latent structure present in the data of the Fukushima Daiichi disaster.} }
Endnote
%0 Conference Paper %T A Bayesian Analysis of the Radioactive Releases of Fukushima %A Ryota Tomioka %A Morten Mrup %B Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2012 %E Neil D. Lawrence %E Mark Girolami %F pmlr-v22-tomioka12 %I PMLR %J Proceedings of Machine Learning Research %P 1243--1251 %U http://proceedings.mlr.press %V 22 %W PMLR %X The Fukushima Daiichi disaster 11 March, 2011 is considered the largest nuclear accident since the 1986 Chernobyl disaster and has been rated at level 7 on the International Nuclear Event Scale. As different radioactive materials have different effects to human body, it is important to know the types of nuclides and their levels of concentration from the recorded mixture of radiations to well take necessary measures. We presently formulate a Bayesian generative model for the data available on radioactive releases from the Fukushima Daiichi disaster across Japan. The model can infer from the sparsely sampled measurements what nuclides are present as well as their concentration levels. An important property of the proposed model is that it admits unique recovery of the parameters. On synthetic data we demonstrate that our model is able to infer the underlying components and on data from the Fukushima Daiichi plant we establish that the model is able to well account for the data. We further demonstrate how the model extends to include all the available measurements recorded throughout Japan. The model can be considered a first attempt to apply Bayesian learning unsupervised in order to give a more detailed account also of the latent structure present in the data of the Fukushima Daiichi disaster.
RIS
TY - CPAPER TI - A Bayesian Analysis of the Radioactive Releases of Fukushima AU - Ryota Tomioka AU - Morten Mrup BT - Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics PY - 2012/03/21 DA - 2012/03/21 ED - Neil D. Lawrence ED - Mark Girolami ID - pmlr-v22-tomioka12 PB - PMLR SP - 1243 DP - PMLR EP - 1251 L1 - http://proceedings.mlr.press/v22/tomioka12/tomioka12.pdf UR - http://proceedings.mlr.press/v22/tomioka12.html AB - The Fukushima Daiichi disaster 11 March, 2011 is considered the largest nuclear accident since the 1986 Chernobyl disaster and has been rated at level 7 on the International Nuclear Event Scale. As different radioactive materials have different effects to human body, it is important to know the types of nuclides and their levels of concentration from the recorded mixture of radiations to well take necessary measures. We presently formulate a Bayesian generative model for the data available on radioactive releases from the Fukushima Daiichi disaster across Japan. The model can infer from the sparsely sampled measurements what nuclides are present as well as their concentration levels. An important property of the proposed model is that it admits unique recovery of the parameters. On synthetic data we demonstrate that our model is able to infer the underlying components and on data from the Fukushima Daiichi plant we establish that the model is able to well account for the data. We further demonstrate how the model extends to include all the available measurements recorded throughout Japan. The model can be considered a first attempt to apply Bayesian learning unsupervised in order to give a more detailed account also of the latent structure present in the data of the Fukushima Daiichi disaster. ER -
APA
Tomioka, R. & Mrup, M.. (2012). A Bayesian Analysis of the Radioactive Releases of Fukushima. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, in PMLR 22:1243-1251

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