Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Exact and efficient Bayesian inference for multiple changepoint problems
Statistics and Computing
Bayesian finite mixtures with an unknown number of components: The allocation sampler
Statistics and Computing
Inferring time-varying network topologies from gene expression data
EURASIP Journal on Bioinformatics and Systems Biology
Hi-index | 0.00 |
Non-stationary Dynamic Bayesian Networks (Non-stationary DBNs) are widely used to model the temporal changes of directed dependency structures from multivariate time series data. However, the existing change-points based non-stationary DBNs methods have several drawbacks including excessive computational cost, and low convergence speed. In this paper we proposed a novel non-stationary DBNs method. Our method is based on the perfect simulation model. We applied this approach for network structure inference from synthetic data and biological microarray gene expression data and compared it with other two state-of-the-art non-stationary DBNs methods. The experimental results demonstrated that our method outperformed two other state-of-the-art methods in both computational cost and structure prediction accuracy. The further sensitivity analysis showed that once converged our model is robust to large parameter ranges, which reduces the uncertainty of the model behavior.