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Advances in Credit Risk Modeling and Management
| Advances in Credit Risk Modeling and Management |
| Autore | Vrins Frédéric |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (190 p.) |
| Soggetto topico | Coins, banknotes, medals, seals (numismatics) |
| Soggetto non controllato |
recovery rates
beta regression credit risk contingent convertible debt financial modelling risk management financial crisis recovery rate loss given default model ambiguity default time no-arbitrage reduced-form HJM models recovery process Counterparty Credit Risk Hidden Markov Model Risk Factor Evolution Backtesting FX rate Geometric Brownian Motion trade credit small and micro-enterprises financial non-financial variables risk assessment logistic regression probability of default wrong-way risk dependence urn model counterparty risk credit valuation adjustment (CVA) XVA (X-valuation adjustments) compression genetic algorithm |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557403603321 |
Vrins Frédéric
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Dynamic Systems Models : New Methods of Parameter and State Estimation / Josif A. Boguslavskiy ; Mark Borodovsky editor
| Dynamic Systems Models : New Methods of Parameter and State Estimation / Josif A. Boguslavskiy ; Mark Borodovsky editor |
| Autore | Boguslavskiy, Josif A. |
| Pubbl/distr/stampa | Cham, : Springer, 2016 |
| Descrizione fisica | xx, 201 p. ; 24 cm |
| Soggetto topico |
68W40 - Analysis of algorithms [MSC 2020]
37Mxx - Approximation methods and numerical treatment of dynamical systems [MSC 2020] 93E11 - Filtering in stochastic control theory [MSC 2020] 93E10 - Estimation and detection in stochastic control theory [MSC 2020] 93-XX - Systems theory; control [MSC 2020] 65L09 - Numerical methods of inverse problems involving ordinary differential equations [MSC 2020] 62M20 - Inference from stochastic processes and prediction; filtering [MSC 2020] |
| Soggetto non controllato |
Aerospatial Dynamics
Asset pricing Biological Sequence Analysis Hidden Markov Model Inverse Problems Parameter Estimation Polynomial Approximation Speech Recognition |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0162131 |
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Boguslavskiy, Josif A.
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| Cham, : Springer, 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Dynamic Systems Models : New Methods of Parameter and State Estimation / Josif A. Boguslavskiy ; Mark Borodovsky editor
| Dynamic Systems Models : New Methods of Parameter and State Estimation / Josif A. Boguslavskiy ; Mark Borodovsky editor |
| Autore | Boguslavskiy, Josif A. |
| Pubbl/distr/stampa | Cham, : Springer, 2016 |
| Descrizione fisica | xx, 201 p. ; 24 cm |
| Soggetto topico |
37Mxx - Approximation methods and numerical treatment of dynamical systems [MSC 2020]
62M20 - Inference from stochastic processes and prediction; filtering [MSC 2020] 65L09 - Numerical methods of inverse problems involving ordinary differential equations [MSC 2020] 68W40 - Analysis of algorithms [MSC 2020] 93-XX - Systems theory; control [MSC 2020] 93E10 - Estimation and detection in stochastic control theory [MSC 2020] 93E11 - Filtering in stochastic control theory [MSC 2020] |
| Soggetto non controllato |
Aerospatial Dynamics
Asset pricing Biological Sequence Analysis Hidden Markov Model Inverse Problems Parameter Estimation Polynomial Approximation Speech Recognition |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00162131 |
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Boguslavskiy, Josif A.
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| Cham, : Springer, 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Machine Learning and Data Mining Applications in Power Systems
| Machine Learning and Data Mining Applications in Power Systems |
| Autore | Leonowicz Zbigniew |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (314 p.) |
| Soggetto topico |
Energy industries and utilities
History of engineering and technology Technology: general issues |
| Soggetto non controllato |
2-150 kHz
agglomerative ANFIS battery energy storage systems (BESS) binary-coded genetic algorithms cluster analysis cluster analysis (CA) clustering conducted disturbances COVID-19 critical infrastructure Data Injection Attack data mining demand response demand-side management demographic characteristic dictionary impulsion different batteries discrete cosine transform discrete Haar transform discrete wavelet transform distributed energy resources distributed energy resources (DER) energy management energy storage systems energy storage systems (ESS) frequency estimation fuzzy logic global index harmonics, cancellation, and attenuation of harmonics Hidden Markov Model home energy management household energy consumption induction generator integrated renewable energy system intentional emission K-means load profile long-term assessment low-voltage networks machine learning mains signalling MPPT n/a neural network non-intentional emission nonlinear loads off-grid microgrid optimal power scheduling optimization techniques Power Line Communications (PLC) power network disturbances power quality power quality (PQ) power system power systems renewable energy short term conditions short-term forecast singular value decomposition smart grid smart grids social distancing sparse signal decomposition spectrum interpolation supervised dictionary learning supraharmonics THDi time series time-varying reproduction number transient stability assessment variable speed WECS virtual power plant virtual power plant (VPP) water treatment plant waveform distortion wind energy wind energy conversion system |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910576877503321 |
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Leonowicz Zbigniew
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection [[electronic resource] /] / by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li
| Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection [[electronic resource] /] / by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li |
| Autore | Zhou Xuefeng |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Springer Nature, 2020 |
| Descrizione fisica | 1 online resource (XVII, 137 p. 50 illus., 44 illus. in color.) |
| Disciplina | 629.892 |
| Soggetto topico |
Robotics
Automation Statistics Control engineering Mechatronics Machine learning Mathematical models Robotics and Automation Bayesian Inference Control, Robotics, Mechatronics Machine Learning Mathematical Modeling and Industrial Mathematics |
| Soggetto non controllato |
Robotics and Automation
Bayesian Inference Control, Robotics, Mechatronics Machine Learning Mathematical Modeling and Industrial Mathematics Robotic Engineering Control, Robotics, Automation Collaborative Robot Introspection Nonparametric Bayesian Inference Anomaly Monitoring and Diagnosis Multimodal Perception Anomaly Recovery Human-robot Collaboration Robot Safety and Protection Hidden Markov Model Robot Autonomous Manipulation open access Robotics Bayesian inference Automatic control engineering Electronic devices & materials Machine learning Mathematical modelling Maths for engineers |
| ISBN | 981-15-6263-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to Robot Introspection -- Nonparametric Bayesian Modeling of Multimodal Time Series -- Incremental Learning Robot Complex Task Representation and Identification -- Nonparametric Bayesian Method for Robot Anomaly Monitoring -- Nonparametric Bayesian Method for Robot Anomaly Diagnose -- Learning Policy for Robot Anomaly Recovery based on Robot. |
| Record Nr. | UNISA-996418268803316 |
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Zhou Xuefeng
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| Springer Nature, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection / Xuefeng Zhou ... [et al.]
| Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection / Xuefeng Zhou ... [et al.] |
| Pubbl/distr/stampa | Singapore, : Springer, 2020 |
| Descrizione fisica | xv, 137 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62F15 - Bayesian inference [MSC 2020] 62M05 - Markov processes: estimation; hidden Markov models [MSC 2020] 93-XX - Systems theory; control [MSC 2020] 62H30 - Classification and discrimination; cluster analysis (statistical aspects) [MSC 2020] 62G05 - Nonparametric estimation [MSC 2020] |
| Soggetto non controllato |
Anomaly Monitoring and Diagnosis
Anomaly Recovery Collaborative Robot Introspection Hidden Markov Model Human-robot Collaboration Multimodal Perception Nonparametric Bayesian Inference Robot Autonomous Manipulation Robot Safety and Protection |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0250242 |
| Singapore, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection / Xuefeng Zhou ... [et al.]
| Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection / Xuefeng Zhou ... [et al.] |
| Pubbl/distr/stampa | Singapore, : Springer, 2020 |
| Descrizione fisica | xv, 137 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62F15 - Bayesian inference [MSC 2020] 62G05 - Nonparametric estimation [MSC 2020] 62H30 - Classification and discrimination; cluster analysis (statistical aspects) [MSC 2020] 62M05 - Markov processes: estimation; hidden Markov models [MSC 2020] 93-XX - Systems theory; control [MSC 2020] |
| Soggetto non controllato |
Anomaly Monitoring and Diagnosis
Anomaly Recovery Collaborative Robot Introspection Hidden Markov Model Human-robot Collaboration Multimodal Perception Nonparametric Bayesian Inference Robot Autonomous Manipulation Robot Safety and Protection |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00250242 |
| Singapore, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Selecting Models from Data : Artificial Intelligence and Statistics 4. / P. Cheeseman, R. W. Oldford editors
| Selecting Models from Data : Artificial Intelligence and Statistics 4. / P. Cheeseman, R. W. Oldford editors |
| Pubbl/distr/stampa | New York [etc.], : Springer-Verlag, 1994 |
| Descrizione fisica | ix, 487 p. ; 24 cm |
| Soggetto topico |
00B25 - Proceedings of conferences of miscellaneous specific interest [MSC 2020]
62-XX - Statistics [MSC 2020] |
| Soggetto non controllato |
Artificial Intelligence
Autonomous agents Best fits Calculus Database Decision tree Expert systems Hidden Markov Model Knowledge Knowledge-based systems Learning Modeling Neural networks Statistical models Statistics |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00293342 |
| New York [etc.], : Springer-Verlag, 1994 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Sensing and Signal Processing in Smart Healthcare
| Sensing and Signal Processing in Smart Healthcare |
| Autore | Zhao Wenbing |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (198 p.) |
| Soggetto topico |
English
For ELT / ESL learning, courses, examinations and certificates Language and Linguistics Language teaching and learning |
| Soggetto non controllato |
ambient assisted living
arrhythmia auditory perception autoencoder Bayesian inference behavioral analysis biometrics brain cancer detection Cascaded-Integrator-Comb (CIC) filter computer vision computer-assisted screening Cramér-Rao lower bound (CRLB) CUDA data adaptive demodulator deep learning denoising autoencoder Dynamic Time Warping eHealth endothelial dysfunction fixed point math FPGA gesture recognition Graphics Processing Units (GPUs) heartbeat classification Hidden Markov Model human monitoring human motion human-computer interaction hyperspectral imaging impaired sensor indoor localisation indoor monitoring Inertial Measurement Unit (IMU) inertial sensors intelligent luminaires Internet of Things (IoT) K-means Kalman filter keypoints feature matching machine learning motion estimation n/a near-infrared images OpenCL OpenMP photoplethysmography scale invariant feature transform simulation sleep pose recognition smart homes software engineering spline function Structural Health Monitoring subharmonics Time of Arrival (TOA) Time of Flight unsupervised clustering usability wearable sensors web control access web security Wi-Fi wireless sensor network |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557483503321 |
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Zhao Wenbing
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Supervised Learning with Quantum Computers / Maria Schuld, Francesco Petruccione
| Supervised Learning with Quantum Computers / Maria Schuld, Francesco Petruccione |
| Autore | Schuld, Maria |
| Pubbl/distr/stampa | Cham, : Springer, 2018 |
| Descrizione fisica | xiii, 297 p. : ill. ; 24 cm |
| Altri autori (Persone) | Petruccione, Francesco |
| Soggetto topico |
81P68 - Quantum computation [MSC 2020]
68Qxx - Theory of computing [MSC 2020] 81-XX - Quantum theory [MSC 2020] 68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020] 68Q12 - Quantum algorithms and complexity in the theory of computing [MSC 2020] 68Q32 - Computational learning theory [MSC 2020] 82C32 - Neural nets applied to problems in time-dependent statistical mechanics [MSC 2020] |
| Soggetto non controllato |
Adiabatic quantum computing
Artificial neural network Belief nets Boltzmann machines Data driven prediction Deutsch-Josza algorithm Grover search Hidden Markov Model Hopfield models Kernel methods Near term application Qsample encoding Quantum Walks Quantum annealing Quantum blas Quantum gates Quantum inference Quantum machine learning Quantum phase estimation |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0211752 |
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Schuld, Maria
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| Cham, : Springer, 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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