Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
An Introduction to Bayesian Inference, Methods and Computation / Nick Heard
| An Introduction to Bayesian Inference, Methods and Computation / Nick Heard |
| Autore | Heard, Nick |
| Pubbl/distr/stampa | Cham, : Springer, 2021 |
| Descrizione fisica | xii, 169 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62F15 - Bayesian inference [MSC 2020] 62A01 - Foundations and philosophical topics in statistics [MSC 2020] |
| Soggetto non controllato |
Bayes factors
Bayes linear regression Bayesian Statistics Bayesian latent factor models Bayesian nonparametrics Computational Bayesian inference Conjugate prior models Gaussian processes PyStan Stan |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0274539 |
Heard, Nick
|
||
| Cham, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Analysis of an Intelligence Dataset
| Analysis of an Intelligence Dataset |
| Autore | Myszkowski Nils |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (166 p.) |
| Soggetto topico | Psychology |
| Soggetto non controllato |
Raven matrices
Standard Progressive Matrices test dimensionality bi-factor parallel analysis target rotation exploratory graph analysis E-assessment general mental ability nested logit models item-response theory ability-based guessing Standard Progressive Matrices Item Response Theory Bayesian statistics brms Stan R Raven's progressive matrices intelligence distractors item analysis intelligence tests classical test theory IRT interaction model test-item regression Mokken scale analysis non-parametric item response theory psychometrics invariant item ordering regularized latent class analysis regularization fused regularization fused grouped regularization distractor analysis |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557153803321 |
|
Myszkowski Nils
|
||
| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian Claims Reserving Methods in Non-life Insurance with Stan : an introduction / Guangyuan Gao
| Bayesian Claims Reserving Methods in Non-life Insurance with Stan : an introduction / Guangyuan Gao |
| Autore | Gao, Guangyuan |
| Pubbl/distr/stampa | Singapore, : Springer, 2018 |
| Descrizione fisica | xii, 205 p. : ill. ; 24 cm |
| Soggetto topico |
91B05 - Risk models (general) [MSC 2020]
62-XX - Statistics [MSC 2020] 62F15 - Bayesian inference [MSC 2020] 62P05 - Applications of statistics to actuarial sciences and financial mathematics [MSC 2020] |
| Soggetto non controllato |
Basis expansion models
Bayesian claims reserving models Copulas Markov chain Monte Carlo methods Multivariate claims reserving model Non-life insurance claims reserving models Payments per claim incurred method Stan |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0125121 |
|
Gao, Guangyuan
|
||
| Singapore, : Springer, 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Bayesian Statistical Modeling with Stan, R, and Python / Kentaro Matsuura
| Bayesian Statistical Modeling with Stan, R, and Python / Kentaro Matsuura |
| Autore | Matsuura, Kentaro |
| Pubbl/distr/stampa | Singapore, : Springer, 2022 |
| Descrizione fisica | xix, 385 p. : ill. ; 24 cm |
| Soggetto non controllato |
Bayesian Modeling
Python Stan Statistical modeling |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0278348 |
|
Matsuura, Kentaro
|
||
| Singapore, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||