Titlebook: Handbook of Computational Statistics; Concepts and Methods James E. Gentle,Wolfgang Karl H?rdle,Yuichi Mori Book 2012Latest edition Springe

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書目名稱Handbook of Computational Statistics影響因子(影響力)




書目名稱Handbook of Computational Statistics影響因子(影響力)學科排名




書目名稱Handbook of Computational Statistics網(wǎng)絡公開度




書目名稱Handbook of Computational Statistics網(wǎng)絡公開度學科排名




書目名稱Handbook of Computational Statistics被引頻次




書目名稱Handbook of Computational Statistics被引頻次學科排名




書目名稱Handbook of Computational Statistics年度引用




書目名稱Handbook of Computational Statistics年度引用學科排名




書目名稱Handbook of Computational Statistics讀者反饋




書目名稱Handbook of Computational Statistics讀者反饋學科排名

割讓

hauteur

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.

尊嚴

insular

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

燕麥

https://doi.org/10.1007/978-3-642-21551-3Bioinformatics; Computational Statistics; EM algorithm; Functional MRI; MCMC; Network Intrusion Detection

panorama

敵手

Exclaim

Yoshio Yotsuyanagi,Daniel Sz?ll?siression 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.