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Case Studies in Applied Bayesian Data Science : CIRM Jean-Morlet Chair, Fall 2018 / Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert editors
| Case Studies in Applied Bayesian Data Science : CIRM Jean-Morlet Chair, Fall 2018 / Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert editors |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | vi, 417 p. : ill. ; 24 cm |
| Soggetto topico |
60Gxx - Stochastic processes [MSC 2020]
60J10 - Markov chains (discrete-time Markov processes on discrete state spaces) [MSC 2020] 62F15 - Bayesian inference [MSC 2020] 62P10 - Applications of statistics to biology and medical sciences; meta analysis [MSC 2020] 62M40 - Random fields; image analysis [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] 62H30 - Classification and discrimination; cluster analysis (statistical aspects) [MSC 2020] 62G05 - Nonparametric estimation [MSC 2020] |
| Soggetto non controllato |
Applied Data Science
Applied Statistics Bayesian Optimization Bayesian Statistics Bayesian computation Bayesian neural networks Big Data Case Studies in Data Science Case studies in Ecology Case studies in Health Composite likelihood Markov random fields Mixture models Spatial models |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0132626 |
| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Case Studies in Applied Bayesian Data Science : CIRM Jean-Morlet Chair, Fall 2018 / Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert editors
| Case Studies in Applied Bayesian Data Science : CIRM Jean-Morlet Chair, Fall 2018 / Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert editors |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | vi, 417 p. : ill. ; 24 cm |
| Soggetto topico |
60Gxx - Stochastic processes [MSC 2020]
60J10 - Markov chains (discrete-time Markov processes on discrete state spaces) [MSC 2020] 62F15 - Bayesian inference [MSC 2020] 62G05 - Nonparametric estimation [MSC 2020] 62H30 - Classification and discrimination; cluster analysis (statistical aspects) [MSC 2020] 62M40 - Random fields; image analysis [MSC 2020] 62P10 - Applications of statistics to biology and medical sciences; meta analysis [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] |
| Soggetto non controllato |
Applied Data Science
Applied Statistics Bayesian Optimization Bayesian Statistics Bayesian computation Bayesian neural networks Big Data Case Studies in Data Science Case studies in Ecology Case studies in Health Composite likelihood Markov random fields Mixture models Spatial models |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00132626 |
| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Machine Learning in Finance : From Theory to Practice / Matthew F. Dixon, Igor Halperin, Paul Bilokon
| Machine Learning in Finance : From Theory to Practice / Matthew F. Dixon, Igor Halperin, Paul Bilokon |
| Autore | Dixon, Matthew F. |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | xxv, 548 p. : ill. ; 24 cm |
| Altri autori (Persone) |
Bilokon, Paul
Halperin, Igor |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020] 62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020] 91-XX - Game theory, economics, finance, and other social and behavioral sciences [MSC 2020] 62M45 - Neural nets and related approaches to inference from stochastic processes [MSC 2020] 62P05 - Applications of statistics to actuarial sciences and financial mathematics [MSC 2020] 91B84 - Economic time series analysis [MSC 2020] 91G80 - Financial applications of other theories [MSC 2020] 91G10 - Portfolio theory [MSC 2020] |
| Soggetto non controllato |
Bayesian neural networks
Financial Econometrics Financial mathematics Investment Management Machine learning Neural networks Reinforcement Learning Time Series Modeling Wealth Management |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0249410 |
Dixon, Matthew F.
|
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| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Machine Learning in Finance : From Theory to Practice / Matthew F. Dixon, Igor Halperin, Paul Bilokon
| Machine Learning in Finance : From Theory to Practice / Matthew F. Dixon, Igor Halperin, Paul Bilokon |
| Autore | Dixon, Matthew F. |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | xxv, 548 p. : ill. ; 24 cm |
| Altri autori (Persone) |
Bilokon, Paul
Halperin, Igor |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020] 62M45 - Neural nets and related approaches to inference from stochastic processes [MSC 2020] 62P05 - Applications of statistics to actuarial sciences and financial mathematics [MSC 2020] 68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020] 91-XX - Game theory, economics, finance, and other social and behavioral sciences [MSC 2020] 91B84 - Economic time series analysis [MSC 2020] 91G10 - Portfolio theory [MSC 2020] 91G80 - Financial applications of other theories [MSC 2020] |
| Soggetto non controllato |
Bayesian neural networks
Financial Econometrics Financial mathematics Investment Management Machine learning Neural networks Reinforcement Learning Time Series Modeling Wealth Management |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00249410 |
|
Dixon, Matthew F.
|
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| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||