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Volumn 66, Issue 3, 2004, Pages 591-607

Markov chain Monte Carlo methods for high dimensional inversion in remote sensing

Author keywords

Adaptive markov chain monte carlo algorithms; Atmospheric remote sensing; Global ozone monitoring of occultation of stars satellite instrument; High dimensional markov chain monte carlo methods; Inverse problems; Regularization

Indexed keywords


EID: 3543035224     PISSN: 13697412     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1467-9868.2004.02053.x     Document Type: Article
Times cited : (99)

References (14)
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    • (1996) Bayesian Statistics , vol.5 , pp. 599-608
    • Gelman, A.G.1    Roberts, G.O.2    Gilks, W.R.3
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    • 0033436531 scopus 로고    scopus 로고
    • Adaptive proposal distribution for random walk Metropolis algorithm
    • Haario, H., Saksman, E. and Tamminen, J. (1999) Adaptive proposal distribution for random walk Metropolis algorithm. Comput. Statist., 14, 375-395.
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    • Haario, H.1    Saksman, E.2    Tamminen, J.3
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    • Componentwise adaptation for high dimensional MCMC
    • to be published
    • Haario, H., Saksman, E. and Tamminen, J. (2003) Componentwise adaptation for high dimensional MCMC. Comput. Statist., to be published.
    • (2003) Comput. Statist.
    • Haario, H.1    Saksman, E.2    Tamminen, J.3
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    • Envisat - GOMOS an instrument for global atmospheric ozone monitoring
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    • Harris, R. A. (ed.) (2001) Envisat - GOMOS an instrument for global atmospheric ozone monitoring. Technical Report SP-1244. European Space Agency, Noordwijk.
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    • Bayesian solution for nonlinear and non-gaussian inverse problems by Markov chain Monte Carlo method
    • Tamminen, J. and Kyrölä, E. (2001) Bayesian solution for nonlinear and non-gaussian inverse problems by Markov chain Monte Carlo method. J. Geophys. Res., 106, 14377-14390.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.