.png){kind=icon}
Return
Efficient Sensitivity Analysis in Biomanufacturing with Sequential Shapley Value Estimation
.png){kind=icon}
PRE
AI
DOI：10.1142/S0217595925400135.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In biomanufacturing, the presence of inherent random factors and the limited amount of data make it essential to characterize the resulting stochastic variability and model risk for stable process control and efficient data utilization. In this study, we adopt the Shapley value (SV) to quantify the contributions of random factors and historical data points to the output variance based on the policy-augmented Bayesian network (PABN) model. To improve the efficiency of SV estimation, we exploit the sparsity inherent in SVs of random factors and data points. In particular, we first formulate a top-lambda selection problem to identify the subset of random factors that collectively explain a prescribed proportion of the variance in output, and then develop a sequential budget allocation algorithm to solve it. Second, to mitigate the model risk with limited real-world data, we design a data valuation procedure that integrates sequential budget allocation with compressive sensing, framing the estimation of SVs as a sparse recovery problem. Numerical experiments on both synthetic and real-world PABN models demonstrate that the proposed methods substantially improve the accuracy and efficiency of selecting key random factors and informative data points with constrained simulation budgets.
Keywords:
Biomanufacturing
sensitivity analysis
Shapley value
simulation
sequential budget allocation
Journal
A
IF:
1
Papers:
60
Citations:
0
Organization
Cited Papers
ShareSaveA Simulation Budget Allocation Procedure for Enhancing the Efficiency of Optimal Subset Selection
PREAI
ShareSaveShareSaveData valuation for medical imaging using Shapley value and application to a large-scale chest X-ray dataset.png)
SCIENTIFIC REPORTS
OAAI
SCIENTIFIC REPORTSIF3.9
OAAIShareSave
View more