Sökning: "probabilistic graphical models"

Visar resultat 1 - 5 av 21 avhandlingar innehållade orden probabilistic graphical models.

1. 1. Perspectives on Probabilistic Graphical Models

   Författare :Dong Liu; Ragnar Thobaben; Harri Lähdesmäki; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Bayesian methods; graphical models; inference; learning; statistics; Electrical Engineering; Elektro- och systemteknik;

   Sammanfattning : Probabilistic graphical models provide a natural framework for the representation of complex systems and offer straightforward abstraction for the interactions within the systems. Reasoning with help of probabilistic graphical models allows us to answer inference queries with uncertainty following the framework of probability theory. LÄS MER

3. 2. Bayesian inference in probabilistic graphical models

   Författare :Felix Leopoldo Rios; Tatjana Pavlenko; Alun Thomas; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Graphical models; Bayesian inference; predictive classification; decomposable graphs; Tillämpad matematik och beräkningsmatematik; Applied and Computational Mathematics;

   Sammanfattning : This thesis consists of four papers studying structure learning and Bayesian inference in probabilistic graphical models for both undirected and directed acyclic graphs (DAGs).Paper A presents a novel algorithm, called the Christmas tree algorithm (CTA), that incrementally construct junction trees for decomposable graphs by adding one node at a time to the underlying graph. LÄS MER

4. 3. Bayesian structure learning in graphical models

   Författare :Felix Leopoldo Rios; Tatjana Pavlenko; Klas Markström; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Bayesian statistics; graphical models; Bayesian networks; Markov networks; structure learning; Tillämpad matematik och beräkningsmatematik; Applied and Computational Mathematics;

   Sammanfattning : This thesis consists of two papers studying structure learning in probabilistic graphical models for both undirected graphs anddirected acyclic graphs (DAGs).Paper A, presents a novel family of graph theoretical algorithms, called the junction tree expanders, that incrementally construct junction trees for decomposable graphs. LÄS MER

5. 4. On approximations and computations in probabilistic classification and in learning of graphical models

   Författare :Magnus Ekdahl; Jose Antonio Lozano; Linköpings universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Mathematical statistics; factorizations; probabilistic classification; nodes; DNA strings; Mathematical statistics; Matematisk statistik;

   Sammanfattning : Model based probabilistic classification is heavily used in data mining and machine learning. For computational learning these models may need approximation steps however. LÄS MER

7. 5. Continuous time Graphical Models and Decomposition Sampling

   Författare :Jonas Hallgren; Timo Koski; Blazej Miasojedow; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES;

   Sammanfattning : Two topics in temporal graphical probabilistic models are studied. The topics are treated in separate papers, both with applications in finance. The first paper study inference in dynamic Bayesian networks using Monte Carlo methods. A new method for sampling random variables is proposed. LÄS MER