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stagedtrees (version 2.1.0)

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.

Arguments

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:

Probabilities, log-likelihood and predictions:

Plot, explore and compare:

Modify models:

References

Collazo R. A., G<U+00F6>rgen C. and Smith J. Q. Chain event graphs. CRC Press, 2018.

G<U+00F6>rgen 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<U+2013>639, 2017.

Examples

Run this code
# NOT RUN {
data("PhDArticles")
mf <- full(PhDArticles, join_unobserved = TRUE)
mod <- stages_fbhc(mf)
plot(mod)
# }

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