A factor graph approach to automated design of Bayesian signal processing algorithms
Authors:
- Marco CoxMCMarco Cox
- Thijs van de LaarTLThijs van de Laar
- Bert de VriesBVBert de Vries
- 1 January 2019
- journal article
- research article
- Published by Elsevier in International Journal of Approximate Reasoning
- Vol. 104, 185-204
Abstract
No abstract availableKeywords
All Related Versions
Funding Information
- GN Hearing
This publication has 17 references indexed in Scilit:
- Variational Inference: A Review for StatisticiansJournal of the American Statistical Association, 2017
- Stan: A Probabilistic Programming Language2017
- Julia: A Fresh Approach to Numerical ComputingSIAM Review, 2017
- Uncertainty in perception and the Hierarchical Gaussian FilterFrontiers in Human Neuroscience, 2014
- Build, Compute, Critique, Repeat: Data Analysis with Latent Variable ModelsAnnual Review of Statistics and Its Application, 2014
- Perceptual decision making: drift-diffusion model is equivalent to a Bayesian modelFrontiers in Human Neuroscience, 2014
- The Factor Graph Approach to Model-Based Signal ProcessingProceedings of the IEEE, 2007
- An Introduction to factor graphsIEEE Signal Processing Magazine, 2004
- Codes on graphs: normal realizationsIEEE Transactions on Information Theory, 2001
- WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibilityStatistics and Computing, 2000