Preserving spatial structure for inverse stochastic simulation using blocking Markov chain Monte Carlo method

Inverse Problems in Science and Engineering

Volumn 16, Issue 7, 2008, Pages 865-884

  Fu, Jianlin ^a   Gómez Hernández, J Jaime ^a  

^a UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

Author keywords

Inverse conditional simulation; Markov chain Monto Carlo; Model calibration; Model structure; Spatial statistics

Indexed keywords

CANNING; INVERSE PROBLEMS; MANAGEMENT SCIENCE; MARKOV PROCESSES; MATHEMATICAL MODELS; MONTE CARLO METHODS; NUMERICAL ANALYSIS; PILES; STOCHASTIC CONTROL SYSTEMS; STOCHASTIC PROGRAMMING; UNCERTAIN SYSTEMS; UNCERTAINTY ANALYSIS;

A POSTERIORI; A-PRIORI; AQUIFER PARAMETERS; BAYESIAN FRAMEWORKS; HARD DATA; INVERSE CONDITIONAL SIMULATION; MARKOV CHAIN MONTE CARLO METHODS; MARKOV CHAIN MONTO CARLO; MARKOV-CHAIN MONTE-CARLO; MODEL CALIBRATION; MODEL STRUCTURE; NON-LINEAR; NON-LINEAR INVERSE PROBLEMS; OPTIMIZERS; PIE- ZOMETRIC HEAD; POSTERIOR DISTRIBUTIONS; SPATIAL INFORMATIONS; SPATIAL PATTERNING; SPATIAL STATISTICS; SPATIAL STRUCTURING; STOCHASTIC SIMULATIONS;

DATA STRUCTURES;

EID: 52949135609     PISSN: 17415977     EISSN: 17415985     Source Type: Journal    
DOI: 10.1080/17415970802015781     Document Type: Article

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