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 | ||
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Information Geometry |
Autore | Verdoolaege Geert |
Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
Descrizione fisica | 1 electronic resource (356 p.) |
Soggetto non controllato |
decomposable divergence
tensor Sylvester matrix maximum pseudo-likelihood estimation matrix resultant ?) Markov random fields Fisher information Fisher information matrix Stein equation entropy Sylvester matrix information geometry stationary process (? dually flat structure information theory Bezout matrix Vandermonde matrix |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910346839903321 |
Verdoolaege Geert | ||
MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
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Multi-Level Bayesian Models for Environment Perception / Csaba Benedek |
Autore | Benedek, Csaba |
Pubbl/distr/stampa | Cham, : Springer, 2022 |
Descrizione fisica | xiii, 202 p. : ill. ; 24 cm |
Soggetto non controllato |
Bayesian Modeling
Computer vision Dynamic Scene Analysis Hierarchical models Marked Point Processes Markov random fields Multi-level object population analysis Remote sensing/photogrammetry Spatiotemporal analysis Stochastic Optimization |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNICAMPANIA-VAN0277962 |
Benedek, Csaba | ||
Cham, : Springer, 2022 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Vanvitelli | ||
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