Quantifying the uncertainty in model parameters using gaussian process-based markov chain monte carlo: An application to cardiac electrophysiological models

Computer Science

• Acceptance Rate 50%
• Approximation (Algorithm) 50%
• Clinical Data 50%
• Computational Efficiency 50%
• markov chain monte-carlo 100%
• Parameter Value 50%
• Posterior Distribution 50%
• Posterior Probability 100%
• Probability Density Function 100%
• Proposal Distribution 50%
• Simulation Model 50%

Engineering

• Clinical Data 33%
• Computational Efficiency 33%
• Estimated Parameter 33%
• Gaussians 100%
• Model Parameter 100%
• Patient Specific Model 33%
• Posterior Distribution 33%
• Posterior Probability 66%
• Probability Density Function 66%
• Real Data 33%
• Simulation Model 33%

Keyphrases

• Acceptance-rejection 50%
• Individual-based Modelling 50%