Discrete Bayesian Networks: The Exact Posterior Marginal Distributions

23 Nov 2014  ·  Do Le Paul Minh ·

In a Bayesian network, we wish to evaluate the marginal probability of a query variable, which may be conditioned on the observed values of some evidence variables. Here we first present our "border algorithm," which converts a BN into a directed chain... For the polytrees, we then present in details, with some modifications and within the border algorithm framework, the "revised polytree algorithm" by Peot & Shachter (1991). Finally, we present our "parentless polytree method," which, coupled with the border algorithm, converts any Bayesian network into a polytree, rendering the complexity of our inferences independent of the size of network, and linear with the number of its evidence and query variables. All quantities in this paper have probabilistic interpretations. read more

PDF Abstract
No code implementations yet. Submit your code now

Tasks


Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here