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Table 4 Bayesian sampling algorithms

From: Bayesian joint modelling of longitudinal and time to event data: a methodological review

Sampling algorithmNumber 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 sampling9(12.5%)[19, 36, 40, 45, 47, 52, 65, 66, 83]
Gibbs sampling with adaptive rejection and MH3(4.2%)[16, 18, 63]
Block Gibbs sampling and MH2(2.8%)[32, 34]
Bayesian Lasso1(1.4%)[28]
Newton-Raphson procedure and a derivative-based MCMC1(1.4%)[71]
No-U-Turn sampler2(2.8%)[73, 74]
Hamiltonian Monte Carlo (HMC)1(1.4%)[81]
HMC and No-U-Turn sampler1(1.4%)[87]