Bayesian statistics using Mathematica.
The writers use the Mathematica software system to perform Bayesian calculations of the type encountered in introductory presentations of Bayesian statistics. Mathematica has in excess of 700 system functions, permitting one to program at a very high level. System functions relate to areas such as...
| Publicado en: | American Statistician Vol. 49; pp. 70 - 77 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
American Statistical Association
February 1995
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=508547773&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 508547773 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: N pubinfo: dt: February 1995 vid: 49 pid: 543 pub: American Statistical Association artinfo: ui: 508547773 10.2307/2684817 ppf: 70 ppct: 7 formats: tig: atl: Bayesian statistics using Mathematica. aug: au: Cook, Peyton Broemeling, Lyle D. su: Wolfram language (Computer program language) Bayesian analysis Time series analysis Monte Carlo method Oxygen consumption sug: subj: Wolfram language (Computer program language) Bayesian analysis Time series analysis Monte Carlo method Oxygen consumption ab: The writers use the Mathematica software system to perform Bayesian calculations of the type encountered in introductory presentations of Bayesian statistics. Mathematica has in excess of 700 system functions, permitting one to program at a very high level. System functions relate to areas such as graphics, numerical computation, and symbolic computation, and these functions can easily be employed to perform many of the usual computations associated with Bayesian inference. The writers illustrate the ease with which numerical computation, graphics, and symbolic computation can be executed to analyze one- and two-dimensional probability density functions by examining a time series problem using oxygen uptake data from a burn patient. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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