Avancerad sökning

Hittade 2 avhandlingar som matchar ovanstående sökkriterier.

1. 1. Bayesian Inference in Large Data Problems

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

   Sammanfattning : 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. LÄS MER

3. 2. 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