A Markov Chain Genetic Algorithm Approach for Non-Parametric Posterior Distribution Sampling of Regression Parameters
This paper proposes a genetic algorithm-based Markov Chain approach that can be used for non-parametric estimation of regression coefficients and their statistical confidence bounds. The proposed approach can generate samples from an unknown probability density function if a formal functional form o...
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Format: | Article |
Language: | English |
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MDPI AG
2024-03-01
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Series: | Algorithms |
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Online Access: | https://www.mdpi.com/1999-4893/17/3/111 |