Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference
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Description: Article published in JAIR (Journal of AI Research) about a way to implement belief networks by compiling networks into arithmetic expressions and then answering queries using an evaluation algorithm.
Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference PO Box 11 - 236, Beirut, Lebanon We describe a new paradigm for implementing inference in belief networks, which consists of two steps: (1) compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG); and (2) answering queries using a simple evaluation algorithm. Each node of a Q-DAG represents a numeric operation, a number, or a symbol for evidence. Each leaf node of a Q-DAG represents the answer to a network query, that is, the probability of some event of interest. It appears that Q-DAGs can be generated using any of the standard algorithms for exact inference in belief networks -- we show how they can be generated using clustering and conditioning algorithms. The time and space complexity of a Q-DAG
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| Page title: | Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference |
| Keywords: | jair-f |
| Description: | Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference |
| IP-address: | 128.2.217.13 |
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| Date | activated: 24-Apr-1985 last updated: 16-Sep-2010 expires: 31-Jul-2014 |