Bayesian inference for data-driven training with application to seismic parameter prediction

2021 ◽  
Author(s):  
Jorge Morales ◽  
Wen Yu ◽  
Luciano Telesca
Keyword(s):  
Bayesian Inference ◽  
Data Driven ◽  
Parameter Prediction

scholarly journals Data-driven power control for state estimation: A Bayesian inference approach

Automatica ◽  
2015 ◽  
Vol 54 ◽  
pp. 332-339 ◽  
Author(s):  
Junfeng Wu ◽  
Yuzhe Li ◽  
Daniel E. Quevedo ◽  
Vincent Lau ◽  
Ling Shi
Keyword(s):  
Bayesian Inference ◽  
Power Control ◽  
State Estimation ◽  
Data Driven

Core log integration: a hybrid intelligent data-driven solution to improve elastic parameter prediction

2019 ◽  
Vol 31 (12) ◽  
pp. 8561-8581 ◽  
Author(s):  
Zeeshan Tariq ◽  
Mohamed Mahmoud ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Elastic Parameter ◽  
Data Driven ◽  
Parameter Prediction

scholarly journals Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties

2021 ◽  
Author(s):  
Fabian Jirasek ◽  
Robert Bamler ◽  
Stephan Mandt
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Bayesian Inference ◽  
Physicochemical Properties ◽  
Infinite Dilution ◽  
Ensemble Methods ◽  
Data Driven ◽  
Prediction Methods ◽  
New Approach ◽  
Prior Model

We present a generic way to hybridize physical and data-driven methods for predicting physicochemical properties. The approach ‘distills’ the physical method's predictions into a prior model and combines it with sparse experimental data using Bayesian inference. We apply the new approach to predict activity coefficients at infinite dilution and obtain significant improvements compared to the physical and data-driven baselines and established ensemble methods from the machine learning literature.

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