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Detection of random signals in dependent gaussian noise / Antonio F. Gualtierotti
| Detection of random signals in dependent gaussian noise / Antonio F. Gualtierotti |
| Autore | Gualtierotti, Antonio F. |
| Pubbl/distr/stampa | [Cham], : Springer, 2015 |
| Descrizione fisica | XXXIV, 1176 p. : ill. ; 24 cm |
| Soggetto topico |
60G15 - Gaussian processes [MSC 2020]
60H05 - Stochastic integrals [MSC 2020] 60-XX - Probability theory and stochastic processes [MSC 2020] 60H10 - Stochastic ordinary differential equations [MSC 2020] 46E22 - Hilbert spaces with reproducing kernels (= [proper] functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) [MSC 2020] 60G35 - Signal detection and filtering (aspects of stochastic processes) [MSC 2020] 60G30 - Continuity and singularity of induced measures [MSC 2020] 60B11 - Probability theory on linear topological spaces [MSC 2020] 60G25 - Prediction theory (aspects of stochastic processes) [MSC 2020] 62M07 - Non-Markovian processes: hypothesis testing [MSC 2020] 94A13 - Detection theory in information and communication theory [MSC 2020] |
| Soggetto non controllato |
Cramér-Hida representations
Dependent signals with arbitrary laws Girsanov‘s theory Goursat processes Information and communication, circuits Prediction processes Reproducing Kernel Hilbert spaces Signal detection Uniqueness class of continuous local martingales |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0113725 |
Gualtierotti, Antonio F.
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| [Cham], : Springer, 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Detection of random signals in dependent gaussian noise / Antonio F. Gualtierotti
| Detection of random signals in dependent gaussian noise / Antonio F. Gualtierotti |
| Autore | Gualtierotti, Antonio F. |
| Pubbl/distr/stampa | [Cham], : Springer, 2015 |
| Descrizione fisica | XXXIV, 1176 p. : ill. ; 24 cm |
| Soggetto topico |
46E22 - Hilbert spaces with reproducing kernels (= [proper] functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) [MSC 2020]
60-XX - Probability theory and stochastic processes [MSC 2020] 60B11 - Probability theory on linear topological spaces [MSC 2020] 60G15 - Gaussian processes [MSC 2020] 60G25 - Prediction theory (aspects of stochastic processes) [MSC 2020] 60G30 - Continuity and singularity of induced measures [MSC 2020] 60G35 - Signal detection and filtering (aspects of stochastic processes) [MSC 2020] 60H05 - Stochastic integrals [MSC 2020] 60H10 - Stochastic ordinary differential equations [MSC 2020] 62M07 - Non-Markovian processes: hypothesis testing [MSC 2020] 94A13 - Detection theory in information and communication theory [MSC 2020] |
| Soggetto non controllato |
Cramér-Hida representations
Dependent signals with arbitrary laws Girsanov‘s theory Goursat processes Information and communication, circuits Prediction processes Reproducing Kernel Hilbert spaces Signal detection Uniqueness class of continuous local martingales |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00113725 |
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Gualtierotti, Antonio F.
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| [Cham], : Springer, 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Magnetic Resonance Brain Imaging : Modeling and Data Analysis Using R / Jörg Polzehl, Karsten Tabelow
| Magnetic Resonance Brain Imaging : Modeling and Data Analysis Using R / Jörg Polzehl, Karsten Tabelow |
| Autore | Polzehl, Jörg |
| Edizione | [2. ed] |
| Pubbl/distr/stampa | Cham, : Springer, 2023 |
| Descrizione fisica | xvi, 258 p. : ill. ; 24 cm |
| Altri autori (Persone) | Tabelow, Karsten |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62H35 - Image analysis in multivariate analysis [MSC 2020] 62P10 - Applications of statistics to biology and medical sciences; meta analysis [MSC 2020] 62R10 - Functional data analysis [MSC 2020] 92-XX - Biology and other natural sciences [MSC 2020] 92C55 - Biomedical imaging and signal processing [MSC 2020] |
| Soggetto non controllato |
BIDS standard
Diffusion models Functional Magnetic Resonance Imaging Functional connectivity Magnetic Reconance Imaging data formats Multi-Parameter Mapping Multiple comparisons NeuroConductor Preprocessing pipelines Quantitative Magnetic Resonance Imaging Resting state Magnetic Resonance Imaging Signal detection Structural Magnetic Resonance Imaging Structural adaptive smoothing Structural connectivity |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00278868 |
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Polzehl, Jörg
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| Cham, : Springer, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Magnetic Resonance Brain Imaging : Modeling and Data Analysis Using R / Jörg Polzehl, Karsten Tabelow
| Magnetic Resonance Brain Imaging : Modeling and Data Analysis Using R / Jörg Polzehl, Karsten Tabelow |
| Autore | Polzehl, Jörg |
| Pubbl/distr/stampa | Cham, : Springer, 2019 |
| Descrizione fisica | xviii, 231 p. : ill. ; 24 cm |
| Altri autori (Persone) | Tabelow, Karsten |
| Soggetto topico |
92-XX - Biology and other natural sciences [MSC 2020]
92C55 - Biomedical imaging and signal processing [MSC 2020] 62-XX - Statistics [MSC 2020] 62P10 - Applications of statistics to biology and medical sciences; meta analysis [MSC 2020] 62H35 - Image analysis in multivariate analysis [MSC 2020] 94A08 - Image processing (compression, reconstruction, etc.) in information and communication theory [MSC 2020] |
| Soggetto non controllato |
BIDS standard
Diffusion models Functional Magnetic Resonance Imaging Functional connectivity Magnetic Reconance Imaging data formats Multi-Parameter Mapping Multiple comparisons NeuroConductor Preprocessing pipelines Quantitative Magnetic Resonance Imaging Resting state Magnetic Resonance Imaging Signal detection Structural Magnetic Resonance Imaging Structural adaptive smoothing Structural connectivity |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0126977 |
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Polzehl, Jörg
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| Cham, : Springer, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Magnetic Resonance Brain Imaging : Modeling and Data Analysis Using R / Jörg Polzehl, Karsten Tabelow
| Magnetic Resonance Brain Imaging : Modeling and Data Analysis Using R / Jörg Polzehl, Karsten Tabelow |
| Autore | Polzehl, Jörg |
| Pubbl/distr/stampa | Cham, : Springer, 2019 |
| Descrizione fisica | xviii, 231 p. : ill. ; 24 cm |
| Altri autori (Persone) | Tabelow, Karsten |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62H35 - Image analysis in multivariate analysis [MSC 2020] 62P10 - Applications of statistics to biology and medical sciences; meta analysis [MSC 2020] 92-XX - Biology and other natural sciences [MSC 2020] 92C55 - Biomedical imaging and signal processing [MSC 2020] 94A08 - Image processing (compression, reconstruction, etc.) in information and communication theory [MSC 2020] |
| Soggetto non controllato |
BIDS standard
Diffusion models Functional Magnetic Resonance Imaging Functional connectivity Magnetic Reconance Imaging data formats Multi-Parameter Mapping Multiple comparisons NeuroConductor Preprocessing pipelines Quantitative Magnetic Resonance Imaging Resting state Magnetic Resonance Imaging Signal detection Structural Magnetic Resonance Imaging Structural adaptive smoothing Structural connectivity |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00126977 |
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Polzehl, Jörg
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| Cham, : Springer, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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