Sökning: "stochastic Markov chain model"
Visar resultat 16 - 20 av 31 avhandlingar innehållade orden stochastic Markov chain model.
16. Simulation-based Inference : From Approximate Bayesian Computation and Particle Methods to Neural Density Estimation
Sammanfattning : This doctoral thesis in computational statistics utilizes both Monte Carlo methods(approximate Bayesian computation and sequential Monte Carlo) and machine-learning methods (deep learning and normalizing flows) to develop novel algorithms for inference in implicit Bayesian models. Implicit models are those for which calculating the likelihood function is very challenging (and often impossible), but model simulation is feasible. LÄS MER
17. Random Geometry and Reinforced Jump Processes
Sammanfattning : This thesis comprises three papers studying several mathematical models related to geometric Markov processes and random processes with reinforcements. The main goal of these works is to investigate the dynamics as well as the limiting behaviour of the models as time goes to infinity, the existence of invariant measures and limiting distributions, the speed of convergence and other interesting relevant properties. LÄS MER
18. Essays in Empirical Finance: Volatility, Interdependencies, and Risk in Emerging Markets
Sammanfattning : The four essays in this thesis deal with emerging markets and their empirical characteristics. They mainly explore the relationship among different markets and the thesis cover several asset classes, including stocks, bonds, and exchange rates. LÄS MER
19. Computationally efficient methods in spatial statistics : Applications in environmental modeling
Sammanfattning : In this thesis, computationally efficient statistical models for large spatial environmental data sets are constructed. In the first part of the thesis, a method for estimating spatially dependent temporal trends is developed. A space-varying regression model, where the regression coefficients for the spatial locations are dependent, is used. LÄS MER
20. Modeling and Segmentation using Multiple Models
Sammanfattning : An established area within the system identification field is identification of linear models. In practice, sometimes the performance of such models is not satisfactory and non-linear models are needed. This thesis is devoted to modeling and identification of systems by means of multiple models. LÄS MER