mrfse.ci | R Documentation |
A greedy algorithm to estimate Markovian neighborhoods.
mrfse.ci(a_size, sample, tau, max_degree=ncol(sample)-1)
a_size |
Size of the alphabet. |
sample |
A integer-valued matrix. Each value must belong range |
tau |
A hyperparameter. See references. |
max_degree |
The maximum length of a candidate Markovian neighborhood. Must be
non-negative and less than |
A list filled with estimated Markov neighborhood for each graph vertex
Rodrigo Carvalho
Guy Bresler. 2015. Efficiently Learning Ising Models on Arbitrary Graphs. In Proceedings of the forty-seventh annual ACM symposium on Theory of Computing (STOC '15). Association for Computing Machinery, New York, NY, USA, 771–782. DOI:https://doi.org/10.1145/2746539.2746631
library(mrfse) a_size = c(0, 1) s = matrix(sample(a_size, size=1000, replace=TRUE), ncol=5) mrfse.ci(length(a_size), s, 0.2)
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