Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction

Mathematics

• Joint Model 100%
• Uncertainty 63%
• Prediction 60%
• Longitudinal Data 20%
• Observation 18%
• Non-Gaussian Noise 14%
• Joint Modeling 13%
• Multivariate Time Series 12%
• Large Data 11%
• Large Data Sets 11%
• Time Series Data 11%
• Imputation 11%
• Optimal Policy 10%
• Confidence 9%
• Innovation 9%
• Missing Data 9%
• Trade-offs 9%
• Decision Making 9%
• Gaussian Process 9%
• Policy 9%
• Model-based 8%
• Scaling 8%
• Experiment 7%
• Output 7%
• Costs 6%
• Modeling 6%
• Framework 5%
• Model 3%

Engineering & Materials Science

• Uncertainty 47%
• Large dataset 20%
• Time series 16%
• Decision making 13%
• Costs 7%
• Experiments 6%