Sökning: "Bayesiansk statistik"

Visar resultat 1 - 5 av 10 avhandlingar innehållade orden Bayesiansk statistik.

1. 1. Scalable Bayesian spatial analysis with Gaussian Markov random fields

   Författare :Per Sidén; Mattias Villani; Anders Eklund; Håvard Rue; Linköpings universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Spatial statistics; Bayesian statistics; Gaussian Markov random fields; fMRI; Machine learning; Spatial statistik; Bayesiansk statistik; Gaussiska Markov-fält; fMRI; Maskininlärning;

   Sammanfattning : Accurate statistical analysis of spatial data is important in many applications. Failing to properly account for spatial autocorrelation may often lead to false conclusions. LÄS MER

3. 2. Bayesian Models for Spatiotemporal Data from Transportation Networks

   Författare :Héctor Rodriguez Déniz; Mattias Villani; Augusto Voltes-Dorta; Yusak Susilo; Linköpings universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Bayesian statistics; Transportation networks; Spatiotemporal data; Machine learning; Bayesiansk statistik; Transportnätverk; Spatiotemporal data; Maskininlärning;

   Sammanfattning : Urbanization has caused a historical transformation at a global scale, and humanity is moving towards a fully connected society where cities will concentrate population, infrastructure and economic activity. A key element in the cities’ infrastructure is the transportation system, as it facilitates the mobility of people and goods. LÄS MER

4. 3. Probabilistic machine learning methods for automated radiation therapy treatment planning

   Författare :Tianfang Zhang; Jimmy Olsson; Steve Jiang; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Matematisk statistik; Mathematical Statistics;

   Sammanfattning : In this thesis, different parts of an automated process for radiation therapy treatment planning are investigated from a mathematical and computational perspective. Whereas traditional inverse planning is labor-intensive, often comprising several reiterations between treatment planner and physician before a plan can be approved, much of recent research have been aimed at using a data-driven approach by learning from historically delivered plans. LÄS MER

5. 4. Scalable and Efficient Probabilistic Topic Model Inference for Textual Data

   Författare :Måns Magnusson; Mattias Villani; Marco Kuhlmann; Jordan Boyd-Graber; Linköpings universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Text analysis; Bayesian inference; Markov chain Monte Carlo; topic models; Textanalys; Bayesiansk inferens; Markov chain Monte Carlo; temamodeller;

   Sammanfattning : Probabilistic topic models have proven to be an extremely versatile class of mixed-membership models for discovering the thematic structure of text collections. There are many possible applications, covering a broad range of areas of study: technology, natural science, social science and the humanities. LÄS MER

7. 5. Accelerating Monte Carlo methods for Bayesian inference in dynamical models

   Författare :Johan Dahlin; Thomas B. Schön; Fredrik Lindsten; Richard Everitt; Linköpings universitet; []
   Nyckelord :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;

   Sammanfattning : 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. LÄS MER