Bayesian Inference of State Space Models : Kalman Filtering and Beyond / Kostas Triantafyllopoulos

• Autore: Triantafyllopoulos, Kostas
• Pubblicazione: Cham, : Springer, 2021
• Descrizione fisica: xv, 495 p. : ill. ; 24 cm
• Soggetto topico: 93E11 - Filtering in stochastic control theory [MSC 2020]; 62-XX - Statistics [MSC 2020]; 62F15 - Bayesian inference [MSC 2020]; 62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020]; 62P20 - Applications of statistics to economics [MSC 2020]; 62P05 - Applications of statistics to actuarial sciences and financial mathematics [MSC 2020]; 91B84 - Economic time series analysis [MSC 2020]; 62P30 - Applications of statistics in engineering and industry; control charts [MSC 2020]; 93E03 - Stochastic systems in control theory (general) [MSC 2020]; 62M20 - Inference from stochastic processes and prediction; filtering [MSC 2020]
• Soggetto non controllato: Bayesian estimation; Bayesian forecasting; Control theory; Dynamic models; Financial Time Series; Non Gaussian time series; Sequential Monte Carlo; State space in dynamic systems; State-space models; Stochastic volatility; Systems stability; Volatility models
• Titolo autorizzato: Bayesian Inference of State Space Models
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: VAN0274587
• Lo trovi qui: Univ. Vanvitelli
• Localizzazioni e accesso elettronico: https://doi.org/10.1007/978-3-030-76124-0
• Serie: Springer texts in statistics Berlin [etc.] . -Springer