stagedtrees {stagedtrees}

Staged event trees.

Description

Algorithms to create, learn, fit and explore staged event tree models. Functions to compute probabilities, make predictions from the fitted models and to plot, analyze and manipulate staged event trees.

Details

A staged event tree is a representation of a particular factorization of a joint probability over a product space. In particular, given a vector of categorical random variables X1, X2, \ldots, a staged event tree represents the factorization P(X1, X2, X3, \ldots) = P(X1)P(X2 | X1) P(X3 | X1, X2) \ldots . Additionally, the stages structure indicates which conditional probabilities are equal.

Model selection algorithms:

• full model full

• independence model indep

• Hill-Climbing stages_hc

• Backward Hill-Climbing stages_bhc

• Fast Backward Hill-Climbing stages_fbhc

• Backward Hill-Climbing Random stages_bhcr

• Backward joining stages_bj

• Simple Backward Hill-Climbing stages_simplebhc

• Hierarchical Clustering stages_hclust

• K-Means Clustering stages_kmeans

• Optimal order search search_best

• Greedy order search search_greedy

Probabilities, log-likelihood and predictions:

• Marginal/Conditional probabilities prob

• Log-Likelihood logLik.sevt

• Predict method predict.sevt

• Confidence intervals confint.sevt

Plot, explore and compare:

• Plot plot.sevt

• Compare compare_stages

• Stages inclusion inclusions_stages

• Stages info summary.sevt

• List of parents as_parentslist

• Barplot construction barplot.sevt

• Likelihood-ratio test lr_test

• Context-specific interventional distance cid

Modify models:

• Join and isolate unobserved situations join_unobserved

• Join two stages join_stages

• Join two positions join_positions

• Rename a stage rename_stage

Author(s)

Maintainer: Gherardo Varando [e-mail] (ORCID)

Authors:

• Federico Carli

• Manuele Leonelli (ORCID)

• Eva Riccomagno

References

Collazo R. A., Görgen C. and Smith J. Q. Chain event graphs. CRC Press, 2018.

Görgen C., Bigatti A., Riccomagno E. and Smith J. Q. Discovery of statistical equivalence classes using computer algebra. International Journal of Approximate Reasoning, vol. 95, pp. 167-184, 2018.

Barclay L. M., Hutton J. L. and Smith J. Q. Refining a Bayesian network using a chain event graph. International Journal of Approximate Reasoning, vol. 54, pp. 1300-1309, 2013.

Smith J. Q. and Anderson P. E. Conditional independence and chain event graphs. Artificial Intelligence, vol. 172, pp. 42-68, 2008.

Thwaites P. A., Smith, J. Q. A new method for tackling asymmetric decision problems. International Journal of Approximate Reasoning, vol. 88, pp. 624–639, 2017.

See Also

Useful links:

• https://github.com/stagedtrees/stagedtrees

• Report bugs at https://github.com/stagedtrees/stagedtrees/issues

Examples

data("PhDArticles")
mf <- full(PhDArticles, join_unobserved = TRUE)
mod <- stages_fbhc(mf)
plot(mod)