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Visar resultat 16 - 20 av 40 avhandlingar som matchar ovanstående sökkriterier.

1. 16. Applications of Bayesian Econometrics to Financial Economics

   Författare :Christoffer Bengtsson; Nationalekonomiska institutionen; []
   Nyckelord :SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; economic systems; economic theory; econometrics; Economics; systemic risk; stochastic volatility; jump-diffusion; shrinkage; covariance matrix estimation; estimation risk; portfolio selection; mean-variance optimization; Markov chain Monte Carlo; Bayesian econometrics; ekonomisk politik; ekonomiska system; ekonomisk teori; ekonometri; Nationalekonomi; economic policy;

   Sammanfattning : This PhD thesis consists of four separate papers. What these papers have in common is that Bayesian Econometrics, in combination with Markov chain Monte Carlo (MCMC) methods, is applied to study various problems in financial economics. LÄS MER

3. 17. Timing and Schedulability Analysis of Real-Time Systems using Hidden Markov Models

   Författare :Anna Friebe; Thomas Nolte; Alessandro Papadopoulos; Filip Markovic; Liliana Cucu-Grosjean; Mälardalens universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Computer Science; datavetenskap;

   Sammanfattning : In real-time systems functional requirements are coupled to timing requirements, a specified event needs to occur at the appropriate time.  In order to ensure that timing requirements are fulfilled, there are two main approaches, static and measurement-based. LÄS MER

4. 18. Numerical simulation of well stirred biochemical reaction networks governed by the master equation

   Författare :Andreas Hellander; Per Lötstedt; Uppsala universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Scientific Computing; Beräkningsvetenskap;

   Sammanfattning : Numerical simulation of stochastic biochemical reaction networks has received much attention in the growing field of computational systems biology. Systems are frequently modeled as a continuous-time discrete space Markov chain, and the governing equation for the probability density of the system is the (chemical) master equation. LÄS MER

5. 19. Simulation-based Inference : From Approximate Bayesian Computation and Particle Methods to Neural Density Estimation

   Författare :Samuel Wiqvist; Matematisk statistik; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Bayesian statistics; computational statistics; deep learning; mixed­-effects; sequential Monte Carlo; stochastic dif­ferential equations;

   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

7. 20. Some Extensions of Fractional Ornstein-Uhlenbeck Model : Arbitrage and Other Applications

   Författare :José Igor Morlanes; Andriy Andreev; Hans Nyquist; Henrik Hult; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; fractional Ornstein-Uhlenbeck process; insider information; simulation embedding method; jump times; least-squares estimator; likelihood process; Ito calculus; Malliavin calculus; stochastic calculus; Statistics; statistik;

   Sammanfattning : This doctoral thesis endeavors to extend probability and statistical models using stochastic differential equations. The described models capture essential features from data that are not explained by classical diffusion models driven by Brownian motion.New results obtained by the author are presented in five articles. LÄS MER