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The Annealed MAP algorithm (Yuan et al., 2004) solves the problem of finding the most likely configuration of values of a set of nodes given observations of another subset of nodes. This problem is often called Maximum A-posteriori Probability (MAP). The Annealed MAP algorithm is approximate and solves the problem by means of an approximate optimization procedure called simulated annealing. While the solution is approximate, it performs well in practice and it gives an idea of the order of magnitude of the true maximum. The Annealed MAP algorithm drastically extends the class of MAP problems that can be solved.

The Network.annealed_map method finds a maximum a-posteriori assignment for a specified set of nodes, given the evidence currently set in the network. The map_nodes argument identifies the nodes for which the most probable outcomes should be determined. The output includes an array of the most probable outcomes for the nodes in map_nodes, with the order of outcomes matching the order of the nodes in the argument list. If all unobserved nodes are included in map_nodes, it may be simpler to use the Most probable explanation (MPE) algorithm instead.

Python

annealed_map(map_nodes: List[int] | List[str], tuning: AnnealedMapTuning) -> AnnealedMapResults

Java

AnnealedMapResults annealedMap(int[] mapNodes, AnnealedMapTuning tuning);

AnnealedMapResults annealedMap(String[] mapNodes, AnnealedMapTuning tuning);

C#

AnnealedMapResults AnnealedMap(int[] mapNodes, AnnealedMapTuning tuning);

AnnealedMapResults AnnealedMap(string[] mapNodes, AnnealedMapTuning tuning);

 

R

results <- annealedMap(mapNodesOrHandles, tuning)