Search for dissertations about: "MCMC"

Showing result 21 - 25 of 48 swedish dissertations containing the word MCMC.

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

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

   Abstract : 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. READ MORE

4. 22. Essays on Financial Risks and Derivatives with Applications to Electricity Markets and Credit Markets

   Author : Rikard Green; Nationalekonomiska institutionen; []
   Keywords : SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; Markov Chain Monte Carlo; Jump Diffusion Process; Stochastic Volatility; VaR; Electricity Markets; Market Risk; Forward Curve; Credit Risk; Currency Effects;

   Abstract : Contracts traded on international financial and commodity markets are associated with complex risk structures. In this dissertation we are concerned with two specific types of risks; market risks and credit risks. The first chapter investigates market risks in the context of the Nordic electricity market. READ MORE

5. 23. Robust analysis of uncertainty in scientific assessments

   Author : Ivette Raices Cruz; Biologiska institutionen; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; expert knowledge; robust Bayesian analysis; scientific assessments; subjective probability; uncertainty analysis;

   Abstract : Uncertainty refers to any limitation in knowledge. Identifying and characterizing uncertainty in conclusions is important to ensure transparency and avoid over or under confidence in scientific assessments. Quantitative expressions of uncertainty are less ambiguous compared to uncertainty expressed qualitatively, or not at all. READ MORE

6. 24. Accelerating Monte Carlo methods for Bayesian inference in dynamical models

   Author : Johan Dahlin; Thomas B. Schön; Fredrik Lindsten; Richard Everitt; Linköpings universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Computational statistics; Monte Carlo; Markov chains; Particle filters; Machine learning; Bayesian optimisation; Approximate Bayesian Computations; Gaussian processes; Particle Metropolis-Hastings; Approximate inference; Pseudo-marginal methods;

   Abstract : Making decisions and predictions from noisy observations are two important and challenging problems in many areas of society. Some examples of applications are recommendation systems for online shopping and streaming services, connecting genes with certain diseases and modelling climate change. READ MORE

7. 25. Correct and Efficient Monte Carlo Inference for Universal Probabilistic Programming Languages

   Author : Daniel Lundén; David Broman; Lawrence Murray; Joakim Jaldén; Sam Staton; KTH; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; Probabilistic programming languages; Compilers; Static program analysis; Monte Carlo inference; Operational semantics; Probabilistiska programmeringsspråk; Kompilatorer; Statisk programanalys; Monte Carlo-inferens; Operationell semantik; Informations- och kommunikationsteknik; Information and Communication Technology;

   Abstract : Probabilistic programming languages (PPLs) allow users to express statistical inference problems that the PPL implementation then, ideally, solves automatically. In particular, PPL users can focus on encoding their inference problems, and need not concern themselves with the intricacies of inference. READ MORE