Improving the fitness of high-dimensional biomechanical models via data-driven stochastic exploration

IEEE Transactions on Biomedical Engineering

Volumn 56, Issue 3, 2009, Pages 552-564

  Santos, Veronica J ^a,b   Bustamante, Carlos D ^c   Valero Cuevas, Francisco J ^a,d  

^a School of Operations Research and Industrial Engineering   (United States)

^b Arizona State University   (United States)

^c Program on Infrastructure Policy   (United States)

^d University of Southern California   (United States)

Author keywords

Bayesian statistics; Biomechanical model; Markov chain Monte Carlo (MCMC); Metropolis Hastings algorithm; Parameter estimation; Thumb

Indexed keywords

BAYESIAN STATISTICS; BIOMECHANICAL MODEL; MARKOV CHAIN MONTE CARLO (MCMC); METROPOLIS-HASTINGS ALGORITHM; THUMB;

BAYESIAN NETWORKS; BIOMECHANICS; MARKOV PROCESSES; METAL ANALYSIS; MONTE CARLO METHODS; STOCHASTIC MODELS; THREE TERM CONTROL SYSTEMS; UNCERTAIN SYSTEMS; UNCERTAINTY ANALYSIS;

PARAMETER ESTIMATION;

ALGORITHM; ANATOMY; ARTICLE; BAYES THEOREM; BIOMECHANICS; KINEMATICS; LANDSCAPE; MATHEMATICAL MODEL; MATHEMATICAL PARAMETERS; METROPOLIS HASTINGS ALGORITHM; MONTE CARLO METHOD; PROBABILITY; THUMB; VELOCITY;

ALGORITHMS; BAYES THEOREM; BIOMECHANICS; COMPUTER SIMULATION; HUMANS; MARKOV CHAINS; MODELS, BIOLOGICAL; MONTE CARLO METHOD; NORMAL DISTRIBUTION; REPRODUCIBILITY OF RESULTS; THUMB;

EID: 65349166763     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2008.2006033     Document Type: Article

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