Ultimate Pólya Gamma Samplers–Efficient MCMC for Possibly Imbalanced Binary and Categorical Data
From MaRDI portal
Publication:6651358
DOI10.1080/01621459.2023.2259030MaRDI QIDQ6651358
Sylvia Frühwirth-Schnatter, Helga Wagner, Gregor Zens
Publication date: 10 December 2024
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- A Bayesian analysis of the multinomial probit model using marginal data augmentation
- Sparsity information and regularization in the horseshoe and other shrinkage priors
- The Pólya-gamma Gibbs sampler for Bayesian logistic regression is uniformly ergodic
- Auxiliary mixture sampling with applications to logistic models
- Bayesian analysis of dichotomous quantal response models
- Hierarchical mixtures-of-experts for exponential family regression models: Approximation and maximum likelihood estimation
- A Bayesian analysis of the multinomial probit model with fully identified parameters
- Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation
- A flexible probabilistic framework for large-margin mixture of experts
- A theoretical comparison of the data augmentation, marginal augmentation and PX-DA algorithms
- Bayesian Statistics and Marketing
- The Calculation of Posterior Distributions by Data Augmentation
- Markov chain Monte Carlo for dynamic generalised linear models
- Parameter expansion to accelerate EM: the PX-EM algorithm
- DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
- On Gibbs sampling for state space models
- Likelihood analysis of non-Gaussian measurement time series
- Parameter Expansion for Data Augmentation
- Efficient MCMC for Binomial Logit Models
- Efficient posterior sampling for high-dimensional imbalanced logistic regression
- Conjugate Bayes for probit regression via unified skew-normal distributions
- MCMC for Imbalanced Categorical Data
- Bayesian Analysis of Binary and Polychotomous Response Data
- Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
- Statistical Modelling and Regression Structures
- Bayesian auxiliary variable models for binary and multinomial regression
- Bayesian Conjugacy in Probit, Tobit, Multinomial Probit and Extensions: A Review and New Results
Related Items (1)
This page was built for publication: Ultimate Pólya Gamma Samplers–Efficient MCMC for Possibly Imbalanced Binary and Categorical Data