Bayesian Diagnostics for Chain Event Graphs
Author:
Affiliation:
1. University of Warwick, Department of Statistics, Coventry, CV4 7AL
Publisher
Institute of Mathematical Statistics
Reference23 articles.
1. Barclay, L. M., Hutton, J. L., and Smith, J. Q. (2013). “Refining a Bayesian Network using a Chain Event Graph.” International Journal of Approximate Reasoning, 54(9): 1300–1309.
2. Cowell, R. G. and Smith, J. Q. (2014). “Causal discovery through MAP selection of stratified chain event graphs.” Electronic Journal of Statistics, 8(1): 965–997.
3. Smith, J. Q. and Anderson, P. E. (2008). “Conditional independence and chain event graphs.” Artificial Intelligence, 172(1): 42–68.
4. Costa, L., Smith, J., Nichols, T., Cussens, J., Duff, E. P., and Makin, T. R. (2015). “Searching multiregression dynamic models of resting-state fMRI networks using integer programming.” Bayesian Analysis, 10(2): 441–478.
5. Barclay, L. M., Collazo, R. A., Smith, J. Q., Thwaites, P. A., and Nicholson, A. E. (2015). “The dynamic chain event graph.” Electronic Journal of Statistics, 9(2): 2130–2169.
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