Search for dissertations about: "Adaptive Markov Chain Monte Carlo"

Showing result 1 - 5 of 7 swedish dissertations containing the words Adaptive Markov Chain Monte Carlo.

2. 1. Semi Markov chain Monte Carlo

   Author : Håkan Ljung; Uppsala universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; Mathematics; Adaptive simulation; error-in-the-variables; Kullback-Leibler divergence; Markov chain simulation; Markov chain Monte Carlo; semi-regenerative; MATEMATIK; MATHEMATICS; MATEMATIK; matematisk statistik; Mathematical Statistics;

   Abstract : The first paper introduces a new simulation technique, called semi Markov chain Monte Carlo, suitable for estimating the expectation of a fixed function over a distribution π, Eπf(χ). Given a Markov chain with stationary distribution p, for example a Markov chain corresponding to a Markov chain Monte Carlo algorithm, an embedded Markov renewal process is used to divide the trajectory into different parts. READ MORE

4. 2. Spatio-Temporal Estimation for Mixture Models and Gaussian Markov Random Fields - Applications to Video Analysis and Environmental Modelling

   Author : Johan Lindström; Matematisk statistik; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; vegetation; time series analysis; video segmentation; spatio-temporal modelling; precipitation; Markov chain Monte Carlo; Gaussian Markov random fields; expectation maximisation; change point detection; Bayesian recursive estimation; African Sahel; adaptive Gaussian mixtures;

   Abstract : In this thesis computationally intensive methods are used to estimate models and to make inference for large, spatio-temporal data sets. The thesis is divided into two parts: the first two papers are concerned with video analysis, while the last three papers model and investigate environmental data from the Sahel area in northern Africa. READ MORE

5. 3. Reconstruction of Past European Land Cover Based on Fossil Pollen Data : Gaussian Markov Random Field Models for Compositional Data

   Author : Behnaz Pirzamanbein; Matematisk statistik; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Spatial Statistics; Adaptive Markov Chain Monte Carlo; Dirichlet Observation; Confidence Region; Palaeoecology; Past Human Land Use; Stochastic Partial Differential Equation;

   Abstract : The aim of this thesis is to develop statistical models to reconstruct past land cover composition and human land use based on fossil pollen records over Europe for different time periods over the past 6000 years. Accurate maps of past land cover and human land use are needed when studying the interaction between climate and land surface, and the effects of human land use on past climate. READ MORE

6. 4. Modeling, Analysis and Design of Wireless Sensor Network Protocols

   Author : Pangun Park; Karl Henrik Johansson; Pravin Varaiya; KTH; []
   Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Wireless Sensor Network; Markov Chain Model; Optimization; Electrical engineering; Elektroteknik; SRA - ICT; SRA - Informations- och kommunikationsteknik;

   Abstract : Wireless sensor networks (WSNs) have a tremendous potential to improve the efficiencyof many systems, for instance, in building automation and process control.Unfortunately, the current technology does not offer guaranteed energy efficiencyand reliability for closed-loop stability. READ MORE

7. 5. Bayesian Sequential Inference for Dynamic Regression Models

   Author : Parfait Munezero; Mattias Villani; Helga Wagner; Stockholms universitet; []
   Keywords : SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; Bayesian sequential inference; Dynamic regression models; Particle filter; Online prediction; Particle smoothing; Linear Bayes; Statistics; statistik;

   Abstract : Many processes evolve over time and statistical models need to be adaptive to change. This thesis proposes flexible models and statistical methods for inference about a data generating process that varies over time. The models considered are quite general dynamic predictive models with parameters linked to a set of covariates via link functions. READ MORE