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Bayes Factors for Forensic Decision Analyses with R [[electronic resource] /] / by Silvia Bozza, Franco Taroni, Alex Biedermann
| Bayes Factors for Forensic Decision Analyses with R [[electronic resource] /] / by Silvia Bozza, Franco Taroni, Alex Biedermann |
| Autore | Bozza Silvia |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (XII, 187 p. 22 illus., 5 illus. in color.) |
| Disciplina | 519.5 |
| Collana | Springer Texts in Statistics |
| Soggetto topico |
Statistics
Mathematical statistics—Data processing Forensic sciences Medical jurisprudence Forensic psychology Social sciences—Statistical methods Statistical Theory and Methods Statistics and Computing Forensic Science Forensic Medicine Forensic Psychology Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy Estadística bayesiana Processament de dades Criminalística R (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| Soggetto non controllato |
Bayes factor
scientific evidence decision making forensic science uncertainty management probability theory forensic decision analysis Bayesian modeling R Bayesian statistics probabilistic inference |
| ISBN | 3-031-09839-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Introduction to the Bayes factor and decision analysis -- Chapter 2: Bayes factor for model choice -- Chapter 3: Bayes factor for evaluative purposes -- Chapter 4: Bayes factor for investigative purposes. |
| Record Nr. | UNISA-996495166503316 |
Bozza Silvia
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| Cham, : Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Big Data Analytics and Information Science for Business and Biomedical Applications II
| Big Data Analytics and Information Science for Business and Biomedical Applications II |
| Autore | Ahmed S. Ejaz |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (196 p.) |
| Soggetto topico |
Computer science
Information technology industries |
| Soggetto non controllato |
asymptotic bias and risk
bandwidth selection Bayesian modeling big data adaptation brain network cancer causal structure learning chest X-ray images consistency correlation deep learning dividend estimation edge-preserving image denoising FCI algorithm fMRI functional connectivity functional predictor functional principal component analysis functional regression gestational weight high dimensionality high-dimensional data Human Connectome Project image sequence infant birth weight joint modeling jump regression analysis LASSO estimation linear mixed model linear mixed-effects model local smoothing longitudinal data lung diseases maternal weight gain mobile device multicollinearity network analysis nonparametric regression nonparametric testing online health community options markets PC algorithm pretest and shrinkage estimation pretrained neural networks ridge estimation social support sparse group regularization spatio-temporal data statistics transfer learning wearable device data weighted least squares |
| ISBN | 3-0365-5550-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910637784003321 |
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Ahmed S. Ejaz
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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