Bayesian network learning method, intelligent device and storage device

Abstract

The invention relates to the field of artificial intelligence, and discloses a Bayesian network learning method, an intelligent device and a storage device. The method comprises the steps of obtaininga training sample which comprises the continuous node data; discretizing the continuous node data to obtain the discrete sample data; performing structure learning by using the discrete sample data to obtain a topology of the Bayesian network; and performing parameter learning by using the training sample and combining the topology of the Bayesian network to obtain the parameters of the Bayesiannetwork. In this way, the speed and the accuracy of the training process can be balanced.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to a Bayesian network method, intelligent equipment and a storage device. Background technique [0002] Bayesian network, also known as belief network (Belief Network), is a typical "probabilistic graphical model" (Probabilistic Graphical Model, PGM), which is a graphical way to express the interdependence between events method of relationship. The traditional Bayesian network is generally discrete, and the nodes are all discrete values, that is, the possible values ​​of the nodes are limited to several definite values, such as 0, 1, 2, and so on. In a continuous Bayesian network, the node values ​​are continuous. A hybrid Bayesian network that contains both discrete and continuous nodes. The process of determining the structure and parameters of the Bayesian network based on the training samples is called the learning of the Bayesian network. For the d...

AI Technical Summary

Problems solved by technology

However, the process of discretizing continuous data will lose part of the information contained in the data, and when using the established network for inference, it is likely to input a new discrete sample space that is not covered in the learning network structure and parameters. sample, making the reasoning process more troublesome

Another solution is to use a mathematical model to model the continuous nodes and then learn the structure and parameters. This method will make the structure learning process more complicated and the training process slower.

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