Table 4 Bayesian sampling algorithms
From: Bayesian joint modelling of longitudinal and time to event data: a methodological review
Sampling algorithm | Number of articles (%) | Reference |
|---|---|---|
Markov Chain Monte Carlo (MCMC) | 28(38.8%) | [13, 17, 20, 24, 26, 30, 31, 33, 38, 39, 43, 46, 49, 50, 53, 57, 58, 60, 62, 67, 68, 72, 75, 76, 82, 84,85,86] |
Gibbs sampler and Metropolis Hastings (MH) | 24(33.3%) | [14, 15, 22, 23, 25, 27, 29, 35, 37, 41, 42, 44, 48, 51, 54, 56, 59, 61, 69, 70, 77,78,79,80] |
Gibbs sampling | 9(12.5%) | |
Gibbs sampling with adaptive rejection and MH | 3(4.2%) | |
Block Gibbs sampling and MH | 2(2.8%) | |
Bayesian Lasso | 1(1.4%) | [28] |
Newton-Raphson procedure and a derivative-based MCMC | 1(1.4%) | [71] |
No-U-Turn sampler | 2(2.8%) | |
Hamiltonian Monte Carlo (HMC) | 1(1.4%) | [81] |
HMC and No-U-Turn sampler | 1(1.4%) | [87] |