极大
发表于 2025-3-21 17:27:47
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割让
发表于 2025-3-21 22:20:40
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hauteur
发表于 2025-3-22 01:55:29
https://doi.org/10.1007/978-1-4613-1749-496). MCMC methods have proved useful in practically all aspects of Bayesian inference, for example, in the context of prediction problems and in the computation of quantities, such as the marginal likelihood, that are used for comparing competing Bayesian models.
尊严
发表于 2025-3-22 05:08:25
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insular
发表于 2025-3-22 10:41:56
Markov Chain Monte Carlo Technology96). MCMC methods have proved useful in practically all aspects of Bayesian inference, for example, in the context of prediction problems and in the computation of quantities, such as the marginal likelihood, that are used for comparing competing Bayesian models.
Affiliation
发表于 2025-3-22 14:10:26
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燕麦
发表于 2025-3-22 18:47:31
https://doi.org/10.1007/978-3-642-21551-3Bioinformatics; Computational Statistics; EM algorithm; Functional MRI; MCMC; Network Intrusion Detection
panorama
发表于 2025-3-22 22:12:22
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敌手
发表于 2025-3-23 04:21:30
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Exclaim
发表于 2025-3-23 09:34:07
Yoshio Yotsuyanagi,Daniel Szöllösiression reduces to solving a system of linear equations, see Chap. III.8. Theprincipal components method is based on finding eigenvalues and eigenvectors of a matrix, see Chap. III.6. Nonlinear optimization methods such as Newton’s method often employ the inversion of a Hessian matrix. In all these cases, we neednumerical linear algebra.