Estimating bayesian networks parameters using EM and Gibbs sampling
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Summary
A method based on Expectation Maximization algorithm and Gibbs sampling is proposed to estimate Bayesian networks (BNs) parameters and the Gibbs sampling to approximate the E-step of EM algorithm is employed.
- Type
- article
- Published
- 2017-01-01
- Cited by
- 8
- References
- 9
- Access
- Open access
- OpenAlex
- https://openalex.org/W2746147786
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:67205102
Keywords
Gibbs sampling, Computer science, Expectation–maximization algorithm, Sampling (signal processing), Divergence (linguistics)
References
- Probabilistic ontologies for knowledge fusion
- Recognizing affect from speech prosody using hierarchical graphical models
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- A stochastic algorithm for parametric and non-parametric estimation in the case of incomplete data
- Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
- A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms
- A Bayesian Network Model for Automatic and Interactive Image Segmentation
- Fault Identification Via Nonparametric Belief Propagation
- A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms
- The EM Algorithm and Extensions
- A Tutorial on Learning with Bayesian Networks
Cited by
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- Matrix factorization based Bayesian network embedding for efficient probabilistic inferences
- Multi-rate Gaussian Bayesian network soft sensor development with noisy input and missing data
- Multi-Attribute Preferences Mining Method for Group Users with the Process of Noise Reduction
- Bayesian analysis of geo-dependencies in wind speed
- Does Economic Development Impact CO2 Emissions and Energy Efficiency Performance? Fresh Evidences From Europe
- Does supply chain matter for environmental firm performance: mediating role of financial development in China
- Analysis of complex Systems with Failure Propagation
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