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This paper presents an adaptive experiment design algorithm aimed at enhancing the efficiency and accuracy of probabilistic integration, particularly when applied to complex computer simulations. The proposed method utilizes a learning function derived from the posterior variance to identify optimal design points, resulting in more accurate integration outcomes compared to traditional random sampling strategies. Experimental results demonstrate the algorithm
- Author
- ANIME Bangladesh
- Language
- EN