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Strategies to avoid overfitting of MCMC Bayesian learning in some biological applications

机译：避免在某些生物学应用中过度拟合MCMC贝叶斯学习的策略

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Garcia Diego M;
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1. Machine learning for effectively avoiding overfitting is a crucial strategy for the genetic prediction of polygenic psychiatric phenotypes [J] . Yuta Takahashi, Masao Ueki, Gen Tamiya, Translational psychiatry. . 2020,第1期

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2. Bayesian model learning based on a parallel MCMC strategy [J] . Jukka Corander, Mats Gyllenberg, Timo Koski Statistics and computing . 2006,第4期

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3. Incremental Learning of Bayesian Networks Based on Chaotic Dual-Population Evolution Strategies and Its Application to Nanoelectronics [J] . Yungang Zhu, Dayou Liu, Haiyang Jia, Journal of Nanoelectronics and Optoelectronics . 2012,第2期

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5. Bayesian Computations via MCMC, with Applications to Big Data and Spatial Data [D] . Entezari, Reihaneh. 2018

机译：通过MCMC进行贝叶斯计算，并应用于大数据和空间数据
6. Machine learning for effectively avoiding overfitting is a crucial strategy for the genetic prediction of polygenic psychiatric phenotypes [O] . Yuta Takahashi, Masao Ueki, Gen Tamiya, 2020

机译：有效避免过度装备的机器学习是多基因精神病学表型遗传预测的重要策略
7. A Bayesian Approach to Learning Hidden Markov Model Topology with Applications to Biological Sequence Analysis [O] . Schliep Alexander 2001

机译：贝叶斯学习隐马尔可夫模型拓扑的方法及其在生物序列分析中的应用

Strategies to avoid overfitting of MCMC Bayesian learning in some biological applications

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