Inference on Markov random fields: methods and applications
<p>This thesis considers the problem of performing inference on undirected graphical models with continuous state spaces. These models represent conditional independence structures that can appear in the context of Bayesian Machine Learning. In the thesis, we focus on computational methods and...
Autor principal: | Lienart, T |
---|---|
Otros Autores: | Doucet, A |
Formato: | Tesis |
Lenguaje: | English |
Publicado: |
2017
|
Materias: |
Ejemplares similares
-
Neural networks for inference, inference for neural networks
por: Webb, S
Publicado: (2018) -
Piecewise-deterministic Markov chain Monte Carlo
por: Vanetti, P
Publicado: (2019) -
Information-Geometric Markov Chain Monte Carlo Methods Using Diffusions
por: Samuel Livingstone, et al.
Publicado: (2014-06-01) -
Bayesian inference with geodetic applications /
por: 253571 Koch, Karl-Rudolf, et al.
Publicado: (1990) -
The predictive view of Bayesian inference
por: Fong, CHE
Publicado: (2021)