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1. |
Record Nr. |
UNINA9910512509903321 |
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Autore |
Gallesi, Erica <1993- > |
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Titolo |
Da Pigmalione a Pinocchio : miti arcaici e cartoni animati / Erica Gallesi ; introduzione di Giulio Giorello |
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Pubbl/distr/stampa |
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ISBN |
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Descrizione fisica |
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Collana |
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Disciplina |
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Locazione |
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Collocazione |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Contiene bibl. pp. 85-92) |
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2. |
Record Nr. |
UNINA9910818391803321 |
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Autore |
Drumond Lucas Rego |
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Titolo |
Factorization models for multi-relational data / / by Lucas Rego Drumond |
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Pubbl/distr/stampa |
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Gottingen, [Germany] : , : Cuvillier Verlag, , 2014 |
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©2014 |
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ISBN |
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Edizione |
[1. Auflage.] |
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Descrizione fisica |
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1 online resource (137 pages) : illustrations |
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Disciplina |
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Soggetti |
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Automatic classification |
Machine learning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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3. |
Record Nr. |
UNINA9910304132703321 |
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Autore |
Kaeding Matthias |
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Titolo |
Bayesian Analysis of Failure Time Data Using P-Splines / / by Matthias Kaeding |
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Pubbl/distr/stampa |
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Wiesbaden : , : Springer Fachmedien Wiesbaden : , : Imprint : Springer Spektrum, , 2015 |
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ISBN |
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Edizione |
[1st ed. 2015.] |
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Descrizione fisica |
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1 online resource (117 p.) |
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Collana |
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Disciplina |
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Soggetti |
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Probabilities |
Medicine - Research |
Biology - Research |
Bioinformatics |
Probability Theory |
Biomedical Research |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Relative Risk and Log-Location-Scale Family -- Bayesian P-Splines -- Discrete Time Models -- Continuous Time Models. |
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Sommario/riassunto |
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Matthias Kaeding discusses Bayesian methods for analyzing discrete and continuous failure times where the effect of time and/or covariates is modeled via P-splines and additional basic function expansions, allowing the replacement of linear effects by more general functions. The MCMC methodology for these models is presented in a unified framework and applied on data sets. Among others, existing algorithms for the grouped Cox and the piecewise exponential model under interval censoring are combined with a data augmentation step for the applications. The author shows that the resulting Gibbs sampler works well for the grouped Cox and is merely adequate for the piecewise exponential model. Contents Relative Risk and Log-Location-Scale Family Bayesian P-Splines Discrete Time Models Continuous Time |
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Models Target Groups Researchers and students in the fields of statistics, engineering, and life sciences Practitioners in the fields of reliability engineering and data analysis involved with lifetimes The Author Matthias Kaeding obtained his Master of Science degree at the University of Bamberg in Survey Statistics. |
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