A probabilistic description of radioactive contamination: a multivariate model

M.S. Nikulin; M.M. Novak; T.I. Smirnov; V.G. Voinov; G.I. Krasnov

doi:10.1016/S0969-8043(00)00191-3

A probabilistic description of radioactive contamination: a multivariate model

M.S. Nikulin, M.M. Novak, T.I. Smirnov, V.G. Voinov, G.I. Krasnov

Research output: Contribution to journal › Article › peer-review

Abstract

A multivariate discrete probability model is used to facilitate the description of gamma-ray spectroscopic data obtained from radioactively contaminated territory, east of the former Semipalatinsk nuclear test site in Kazakhstan. Two possible estimators of probabilities of interest have been considered: maximum likelihood and unbiased estimators. We show that unbiased estimators are much easier to compute. The model was used in two variants: (i) several radionuclides in spatially independent measurements, (ii) a single radionuclide in spatially dependent measurements. We show that, in both cases, it is important to take into account the correlation for the accurate evaluation of probabilities of interest.

Original language	English
Pages (from-to)	355-363
Journal	Applied Radiation and Isotopes
Volume	54
Issue number	2
DOIs	https://doi.org/10.1016/S0969-8043(00)00191-3
Publication status	Published - Feb 2001

Bibliographical note

Note: This was was supported by INTAS-Kazakhstan [grant number 95-0053].

Keywords

environmental radioactive pollution
multivariate discrete probability distributions
maximum likelihood and unbiased estimators
gamma-ray spectrometry
Computer science and informatics

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10.1016/S0969-8043(00)00191-3

http://dx.doi.org/doi:10.1016/S0969-8043(00)00191-3

Cite this

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title = "A probabilistic description of radioactive contamination: a multivariate model",

abstract = "A multivariate discrete probability model is used to facilitate the description of gamma-ray spectroscopic data obtained from radioactively contaminated territory, east of the former Semipalatinsk nuclear test site in Kazakhstan. Two possible estimators of probabilities of interest have been considered: maximum likelihood and unbiased estimators. We show that unbiased estimators are much easier to compute. The model was used in two variants: (i) several radionuclides in spatially independent measurements, (ii) a single radionuclide in spatially dependent measurements. We show that, in both cases, it is important to take into account the correlation for the accurate evaluation of probabilities of interest.",

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author = "M.S. Nikulin and M.M. Novak and T.I. Smirnov and V.G. Voinov and G.I. Krasnov",

note = "Note: This was was supported by INTAS-Kazakhstan [grant number 95-0053].",

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AU - Nikulin, M.S.

AU - Novak, M.M.

AU - Smirnov, T.I.

AU - Voinov, V.G.

AU - Krasnov, G.I.

N1 - Note: This was was supported by INTAS-Kazakhstan [grant number 95-0053].

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AB - A multivariate discrete probability model is used to facilitate the description of gamma-ray spectroscopic data obtained from radioactively contaminated territory, east of the former Semipalatinsk nuclear test site in Kazakhstan. Two possible estimators of probabilities of interest have been considered: maximum likelihood and unbiased estimators. We show that unbiased estimators are much easier to compute. The model was used in two variants: (i) several radionuclides in spatially independent measurements, (ii) a single radionuclide in spatially dependent measurements. We show that, in both cases, it is important to take into account the correlation for the accurate evaluation of probabilities of interest.

KW - environmental radioactive pollution

KW - multivariate discrete probability distributions

KW - maximum likelihood and unbiased estimators

KW - gamma-ray spectrometry

KW - Computer science and informatics

UR - http://www.ncbi.nlm.nih.gov/pubmed/11200899

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VL - 54

SP - 355

EP - 363

JO - Applied Radiation and Isotopes

JF - Applied Radiation and Isotopes

IS - 2

ER -

A probabilistic description of radioactive contamination: a multivariate model

Abstract

Bibliographical note

Keywords

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