Bayesian Allocation Model: Marginal Likelihood-Based Model Selection for Count Tensors

Yıldırım, Sinan; Kurutmaz, M. Burak; Barsbey, Melih; Şimşekli, Umut; Cemgil, Ali Taylan

doi:10.1109/jstsp.2020.3045297

Cited by 3 publications

(1 citation statement)

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“…Bayesian network is a kind of directed acyclic graph that represents the probability dependence between variables. The Bayesian network composed of sample learning expresses a set of conditional independence assumptions: any node is independent of its non-offspring nodes given the state of its parent node [12,13]. Then the joint probability distribution corresponding to the random vector composed of the nodes of the Bayesian network can be decomposed into the product of the marginal distribution of random variables, namely.…”

Section: The Definition Of Bayesian Network and Its Variable Distribu...mentioning

confidence: 99%

Formulation and Implementation of a Bayesian Network-Based Model

Shi

2023

Int. J. Appl. Inf. Manag.

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At present, Bayesian networks lack consistent algorithms that support structure establishment, parameter learning, and knowledge reasoning, making it impossible to connect knowledge establishment and application processes. In view of this situation, by designing a genetic algorithm coding method suitable for Bayesian network learning, crossover and mutation operators with adjustment strategies, the fitness function for reasoning error feedback can be carried out. Experimental results show that the new algorithm not only simultaneously optimizes the network structure and parameters, but also can adaptively learn and correct inference errors, and has a more satisfactory knowledge inference accuracy rate.

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Section: The Definition Of Bayesian Network and Its Variable Distribu...mentioning

confidence: 99%