Search for dissertations about: "Pseudo-marginal MCMC"

Found 3 swedish dissertations containing the words Pseudo-marginal MCMC.

  2. 1. Bayesian Inference in Large Data Problems

    Author : Matias Quiroz; Mattias Villani; Bani K. Mallick; Stockholms universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Bayesian inference; Large data sets; Markov chain Monte Carlo; Survey sampling; Pseudo-marginal MCMC; Delayed acceptance MCMC; Statistics; statistik;

    Abstract : In the last decade or so, there has been a dramatic increase in storage facilities and the possibility of processing huge amounts of data. This has made large high-quality data sets widely accessible for practitioners. This technology innovation seriously challenges traditional modeling and inference methodology. READ MORE

  4. 2. 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

  5. 3. 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