Articoli correlati a Approaches in Highly Parameterized Inversion: bgaPEST,...

Approaches in Highly Parameterized Inversion: bgaPEST, a Bayesian Geostatistical Approach Implementation With PEST?Documentation and Instructions - Brossura

Fienen, Michael N; D'Oria, Marco; Doherty, John E; Hunt, Randall J

 
9781500297459: Approaches in Highly Parameterized Inversion: bgaPEST, a Bayesian Geostatistical Approach Implementation With PEST?Documentation and Instructions
Brossura
ISBN 10: 1500297453 ISBN 13: 9781500297459
Casa editrice: CreateSpace Independent Publishing Platform, 2014

Sinossi

The application bgaPEST is a highly parameterized inversion software package implementing the Bayesian Geostatistical Approach in a framework compatible with the parameter estimation suite PEST. Highly parameterized inversion refers to cases in which parameters are distributed in space or time and are correlated with one another. The Bayesian aspect of bgaPEST is related to Bayesian probability theory in which prior information about parameters is formally revised on the basis of the calibration dataset used for the inversion. Conceptually, this approach formalizes the conditionality of estimated parameters on the specific data and model available.

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Editore
CreateSpace Independent Publishing Platform
Data di pubblicazione
2014
Lingua
Inglese
ISBN 10 
1500297453
ISBN 13 
9781500297459
Rilegatura
Copertina flessibile
Numero di pagine
94
Contatto del produttore
Manufactured by Amazon on behalf of the author
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