Efficient Posterior Sampling for Bayesian Poisson Regression

DOI10.1080/10618600.2022.2123337arXiv2109.09520OpenAlexW3200476120MaRDI QIDQ6180722

Antonio Canale, Laura A. M. D'Angelo

Publication date: 22 January 2024

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2109.09520

zbMATH Keywords

importance samplingcount dataMetropolis-Hastingslog-linear modelshorseshoe priorPólya-gamma

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Sparsity information and regularization in the horseshoe and other shrinkage priors
• Computationally efficient multivariate spatio-temporal models for high-dimensional count-valued data (with discussion)
• Adaptive posterior contraction rates for the horseshoe
• Auxiliary mixture sampling for parameter-driven models of time series of counts with applications to state space modelling
• The Least Upper Bound on the Poisson-Negative Binomial Relative Error
• The Analysis of Rates Using Poisson Regression Models
• Handbook of Markov Chain Monte Carlo
• The horseshoe estimator for sparse signals
• The Bayesian Lasso
• Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
• Variational Gaussian approximation for Poisson data
• Combining historical data and bookmakers’ odds in modelling football scores
• Bayesian Analysis of Binary and Polychotomous Response Data
• Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
• Monte Carlo sampling methods using Markov chains and their applications
• Introducing Monte Carlo Methods with R
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