scholarly journals Evaluating Predictive Uncertainty Challenge

2006 ◽  
pp. 1-27 ◽  
Author(s):  
Joaquin Quiñonero-Candela ◽  
Carl Edward Rasmussen ◽  
Fabian Sinz ◽  
Olivier Bousquet ◽  
Bernhard Schölkopf
Keyword(s):  
Predictive Uncertainty

Preliminary uncertainty analysis – a prerequisite for assessing the predictive uncertainty of hydrologic models

1996 ◽  
Vol 33 (2) ◽  
pp. 79-90 ◽  
Author(s):  
Jian Hua Lei ◽  
Wolfgang Schilling
Keyword(s):  
Uncertainty Analysis ◽  
Model Parameters ◽  
Observation Data ◽  
Calibration Data ◽  
Model Output ◽  
Predictive Uncertainty ◽  
Runoff Volume ◽  
Output Uncertainty ◽  
Sensitive Parameters ◽  
Calculated Model

Physically-based urban rainfall-runoff models are mostly applied without parameter calibration. Given some preliminary estimates of the uncertainty of the model parameters the associated model output uncertainty can be calculated. Monte-Carlo simulation followed by multi-linear regression is used for this analysis. The calculated model output uncertainty can be compared to the uncertainty estimated by comparing model output and observed data. Based on this comparison systematic or spurious errors can be detected in the observation data, the validity of the model structure can be confirmed, and the most sensitive parameters can be identified. If the calculated model output uncertainty is unacceptably large the most sensitive parameters should be calibrated to reduce the uncertainty. Observation data for which systematic and/or spurious errors have been detected should be discarded from the calibration data. This procedure is referred to as preliminary uncertainty analysis; it is illustrated with an example. The HYSTEM program is applied to predict the runoff volume from an experimental catchment with a total area of 68 ha and an impervious area of 20 ha. Based on the preliminary uncertainty analysis, for 7 of 10 events the measured runoff volume is within the calculated uncertainty range, i.e. less than or equal to the calculated model predictive uncertainty. The remaining 3 events include most likely systematic or spurious errors in the observation data (either in the rainfall or the runoff measurements). These events are then discarded from further analysis. After calibrating the model the predictive uncertainty of the model is estimated.

Kriging-based reliability analysis considering predictive uncertainty reduction

2021 ◽  
Author(s):  
Meng Li ◽  
Sheng Shen ◽  
Vahid Barzegar ◽  
Mohammadkazem Sadoughi ◽  
Chao Hu ◽  
...  
Keyword(s):  
Reliability Analysis ◽  
Uncertainty Reduction ◽  
Predictive Uncertainty

Predictive Uncertainty Estimation Using Deep Learning for Soft Robot Multimodal Sensing

2021 ◽  
Vol 6 (2) ◽  
pp. 951-957
Author(s):  
Ze Yang Ding ◽  
Junn Yong Loo ◽  
Vishnu Monn Baskaran ◽  
Surya Girinatha Nurzaman ◽  
Chee Pin Tan
Keyword(s):  
Deep Learning ◽  
Uncertainty Estimation ◽  
Soft Robot ◽  
Predictive Uncertainty ◽  
Multimodal Sensing

scholarly journals Self-Supervised Mixture-of-Experts by Uncertainty Estimation

2019 ◽  
Vol 33 ◽  
pp. 5933-5940
Author(s):  
Zhuobin Zheng ◽  
Chun Yuan ◽  
Xinrui Zhu ◽  
Zhihui Lin ◽  
Yangyang Cheng ◽  
...  
Keyword(s):  
Common Knowledge ◽  
Uncertainty Estimation ◽  
Complex Environments ◽  
Mixture Of Experts ◽  
Predictive Uncertainty ◽  
Value Evaluation ◽  
Policy Gradient ◽  
Experience Replay ◽  
Gating Network ◽  
Efficient Transfer

Learning related tasks in various domains and transferring exploited knowledge to new situations is a significant challenge in Reinforcement Learning (RL). However, most RL algorithms are data inefficient and fail to generalize in complex environments, limiting their adaptability and applicability in multi-task scenarios. In this paper, we propose SelfSupervised Mixture-of-Experts (SUM), an effective algorithm driven by predictive uncertainty estimation for multitask RL. SUM utilizes a multi-head agent with shared parameters as experts to learn a series of related tasks simultaneously by Deep Deterministic Policy Gradient (DDPG). Each expert is extended by predictive uncertainty estimation on known and unknown states to enhance the Q-value evaluation capacity against overfitting and the overall generalization ability. These enable the agent to capture and diffuse the common knowledge across different tasks improving sample efficiency in each task and the effectiveness of expert scheduling across multiple tasks. Instead of task-specific design as common MoEs, a self-supervised gating network is adopted to determine a potential expert to handle each interaction from unseen environments and calibrated completely by the uncertainty feedback from the experts without explicit supervision. To alleviate the imbalanced expert utilization as the crux of MoE, optimization is accomplished via decayedmasked experience replay, which encourages both diversification and specialization of experts during different periods. We demonstrate that our approach learns faster and achieves better performance by efficient transfer and robust generalization, outperforming several related methods on extended OpenAI Gym’s MuJoCo multi-task environments.

scholarly journals Predictive Uncertainty Estimation for Tractable Deep Probabilistic Models

2020 ◽  
Author(s):  
Julissa Villanueva Llerena
Keyword(s):  
Probabilistic Models ◽  
Linear Time ◽  
Generative Models ◽  
Uncertainty Estimation ◽  
Image Completion ◽  
Predictive Uncertainty ◽  
Learning Tasks ◽  
Challenging Tasks ◽  
Statistical Support ◽  
Marginal Inference

Tractable Deep Probabilistic Models (TPMs) are generative models based on arithmetic circuits that allow for exact marginal inference in linear time. These models have obtained promising results in several machine learning tasks. Like many other models, TPMs can produce over-confident incorrect inferences, especially on regions with small statistical support. In this work, we will develop efficient estimators of the predictive uncertainty that are robust to data scarcity and outliers. We investigate two approaches. The first approach measures the variability of the output to perturbations of the model weights. The second approach captures the variability of the prediction to changes in the model architecture. We will evaluate the approaches on challenging tasks such as image completion and multilabel classification.

Case studies of predictive uncertainty quantification for geothermal models

Geothermics ◽  
2021 ◽  
Vol 97 ◽  
pp. 102263
Author(s):  
Jericho Omagbon ◽  
John Doherty ◽  
Angus Yeh ◽  
Racquel Colina ◽  
John O'Sullivan ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Case Studies ◽  
Predictive Uncertainty
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