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Sequential Poisson Regression in Diffusion Networks
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DOI：10.1109/LSP.2020.2987723.png)
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Abstract
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The Poisson regression is a popular model for positive integer random variables determined by known explanatory variables. This letter studies the problem of its collaborative Bayesian sequential estimation under potentially slowly time-varying regression coefficients. We assume networks where agents share their information about the inferred quantities with adjacent neighbors in order to improve the overall estimation performance. The communication strategy is the information diffusion, i.e., only one information exchange per time instant is allowed.
Keywords:
Estimation
Bayes methods
Gaussian distribution
Random variables
Inference algorithms
Predictive models
Probability density function
Diffusion
distributed estimation
collaborative estimation
Poisson regression
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