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

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书目名称Handbook of Computational Statistics影响因子(影响力)




书目名称Handbook of Computational Statistics影响因子(影响力)学科排名




书目名称Handbook of Computational Statistics网络公开度




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书目名称Handbook of Computational Statistics被引频次




书目名称Handbook of Computational Statistics被引频次学科排名




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割让

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.

尊严

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

燕麦

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

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敌手

Exclaim

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.