| ... | ... | @@ -6,3 +6,15 @@ A Bayesian network is a pair of directed acyclic graph (DAG) describing the depe |
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The task of training a Bayesian network is thus split into two subtasks:
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* Finding the structure of the Bayesian network.
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* Parametric training of the Bayesian network or, in other words, selection of marginal and conditional distributions that describe the conditional ones accurately enough.
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# Structural learning algorithms for a Bayesian network
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Often the task of constructing a network is reduced to optimization. In the DAG space, score functions are introduced that evaluate how well the graph describes the dependencies between features. Web BAMT uses the Hill-Climbing algorithm to search in this space.
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Steps of Hill-Climbing algorithm:
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1. Initialized by a graph without edges;
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2. For each pair of nodes, an edge is added, removed or the direction is changed;
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3. The score-function value is counted after the selected action on the pair;
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4. If the value of score-function is better than that of the previous iteration, the result is memorized;
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5. Stops when the value changes less than the threshold value.
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The following score functions from [BAMT package](https://github.com/ITMO-NSS-team/BAMT) are included in Web BAMT: K2, BIC (Bayesian Information Criterion), MI (Mutual Information).
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The above approach to structure search allows to introduce elements of expert control by narrowing the search area to structures that include expert-specified edges or fixed root nodes describing key and basic features. |
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