Sökning: "stochastic filtering theory"
Visar resultat 1 - 5 av 16 avhandlingar innehållade orden stochastic filtering theory.
1. Optimal stopping, incomplete information, and stochastic games
Sammanfattning : This thesis contains six papers on the topics of optimal stopping and stochastic games. Paper I extends the classical Bayesian sequential testing and detection problems for a Brownian motion to higher dimensions. We demonstrate unilateral concavity of the cost function and present its structural properties through various examples. LÄS MER
2. On estimation in econometric systems in the presence of time-varying parameters
Sammanfattning : Economic systems are often subject to structural variability. For the achievement of correct structural specification in econometric modelling it is then important to allow for parameters that are time-varying, and to apply estimation techniques suitably designed for inference in such models. LÄS MER
3. Optimal Sequential Decisions in Hidden-State Models
Sammanfattning : This doctoral thesis consists of five research articles on the general topic of optimal decision making under uncertainty in a Bayesian framework. The papers are preceded by three introductory chapters.Papers I and II are dedicated to the problem of finding an optimal stopping strategy to liquidate an asset with unknown drift. LÄS MER
4. Bridges with Random Length and Pinning Point for Modelling the Financial Information
Sammanfattning : The impact of the information concerning an event of interest occurring at a future random time is the main topic of this work. The event can massively influence financial markets and the problem of modelling the information on the time at which it occurs is of crucial importance in financial modelling. LÄS MER
5. Toward Sequential Data Assimilation for NWP Models Using Kalman Filter Tools
Sammanfattning : The aim of the meteorological data assimilation is to provide an initial field for Numerical Weather Prediction (NWP) and to sequentially update the knowledge about it using available observations. Kalman filtering is a robust technique for the sequential estimation of the unobservable model state based on the linear regression concept. LÄS MER