Implementation of a collection of MCMC methods for Bayesian structure learning
of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient
inference on larger DAGs, the space of DAGs is pruned according to the data. To filter
the search space, the algorithm employs a hybrid approach, combining constraint-based
learning with search and score. A reduced search space is initially defined on the basis
of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with
search and score. Search and score is then performed following two approaches:
Order MCMC, or Partition MCMC.
The BGe score is implemented for continuous data and the BDe score is implemented
for binary data. The algorithms may provide the maximum a posteriori (MAP) graph or
a sample (a collection of DAGs) from the posterior distribution given the data.
N. Friedman and D. Koller (2003)
|Author||Polina Suter [aut, cre], Jack Kuipers [aut]|
|Date of publication||2018-05-24 11:16:07 UTC|
|Maintainer||Polina Suter <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.