Parameter and uncertainty estimation for dynamical systems using surrogate stochastic processes

Keyphrases

• Parameter Estimation 100%
• Dynamical Systems 100%
• Stochastic Processes 100%
• Uncertainty Estimation 100%
• Observational Data 66%
• Modularity 33%
• Influenza-like Illness 33%
• Point Estimate 33%
• Prediction Accuracy 33%
• Heteroscedasticity 33%
• Statistical Learning 33%
• Simulation Study 33%
• Real-world Application 33%
• Robust Control 33%
• Unknown Quantity 33%
• Markov Chain Monte Carlo 33%
• Bayesian Approach 33%
• Design Futures 33%
• Viral Kinetics 33%
• Mathematical Theory 33%
• Appropriate Design 33%
• Empirical Measurement 33%
• Surrogate Data 33%
• Parallelizability 33%
• Within-host 33%
• Heavy Tails 33%
• Two-step Approach 33%
• Front-loading 33%
• Computational Limit 33%
• Parameter Estimation Problem 33%
• Ordinary Point 33%
• Heavy Censoring 33%
• Predator-prey 33%
• Ill-posedness 33%
• Point Estimate Method 33%

Engineering

• Limitations 100%
• Real World Application 100%
• Parameter Estimation 100%
• Kinetic Model 100%
• Regularization 100%
• Bayesian Approach 100%
• Accurate Prediction 100%
• Posedness 100%
• Point Estimate 100%
• Step Approach 100%

Mathematics

• Dynamical System 100%
• Stochastic Process 100%
• Observational Data 66%
• Parameter Estimation 33%
• Regularization 33%
• Data Pair 33%
• Simulation Study 33%
• Markov Chain Monte Carlo 33%
• Estimation Method 33%
• Point Estimate 33%
• Accurate Prediction 33%
• Surrogate Data 33%
• Heavy Tail 33%
• Point Estimation 33%
• Mathematical Theory 33%
• Predator-Prey 33%
• Bayesian Approach 33%
• Posedness 33%
• Ordinary Point 33%
• Robust Control 33%