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Marketing Strategy of Private Enterprises Based on Bayesian Dynamic Panel Model of Machine Learning Algorithms

Siyu Sun & Juan Long

International Journal of Information Technology and Web Engineering2024https://doi.org/10.4018/ijitwe.344834article
ABDC C
Weight
0.30

What the paper says

Machine learning algorithms have attracted widespread attention in both industry and academia. This article mainly studies the marketing strategy decision-making of private listed enterprises based on Bayesian panel data model. By constructing a Bayesian static panel data model and a Bayesian dynamic panel data model, an empirical analysis was conducted on the debt financing decisions of private enterprises from two aspects: external financial environment and internal governance. The experimental results show that the MC error and standard deviation of parameter estimation for Bayesian static panel data model and Bayesian dynamic panel data model are both very small. This method contains more information, increases observation data and degrees of freedom. This article provides important theoretical guidance for the coordinated development of private listed enterprises and state-owned enterprises. It is conducive to promoting the coordinated development of the entire national economy.

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https://doi.org/https://doi.org/10.4018/ijitwe.344834

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@article{siyu2024,
  title        = {{Marketing Strategy of Private Enterprises Based on Bayesian Dynamic Panel Model of Machine Learning Algorithms}},
  author       = {Siyu Sun & Juan Long},
  journal      = {International Journal of Information Technology and Web Engineering},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.4018/ijitwe.344834},
}

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Marketing Strategy of Private Enterprises Based on Bayesian Dynamic Panel Model of Machine Learning Algorithms

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Evidence weight

0.30

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.