Adaptive Rejection Metropolis Sampling Within Gibbs Sampling

By Walter R. Gilks, N. G. Best, K. Tan. 1995

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Citations in the corpus

A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use (2010, background): “The full conditionals for the above models do not have closed forms; however, since the likelihoods are log-concave, adaptive rejection sampling or adaptive rejection Metropolis sampling can be used (Gilks and Wild, 1992; Gilks et al., 1995).”
MCMC Analysis of Diffusion Models With Application to Finance (2001, background): “Prakasa Rao (1988), Yoshida (1992), and Kessler (1997) proposed estimators converging to the true parameter more rapidly as the data are sampled more frequently. An alternative strategy that relies on discretizations of the continuoustime likelihood function was given by Liptser and Shiryayev (1977)”
Bayesian Estimation and Model Choice in Item Response Models (2002, methodology): “However, the computations can be performed using a Metropolis step (Gilks et al., 1995).”
Slice Sampling (2000, methodology): “Applying ARMS to sample from the conditional distribution of each variable in succession will result in an equilibrium distribution that is exactly correct, but when some conditional distributions are not log-concave, it may take longer to approach this equilibrium than would be the case if true Gib”
Bayesian varying-coefficient models using adaptive regression splines (2001, methodology): “Suitable approaches are the adaptive rejection Metropolis sampler of Gilks et al. (1995) or the approach of Gamerman (1997a), the so-called weighted least squares proposal.”
Parametric links for binary choice models: A Fisherian-Bayesian colloquy (2009, methodology): “Gilks, Best, and Tan (1995) and Geweke and Tanizaki (2001).”
Sampling from the posterior distribution in generalized linear mixed models (1997, background): “Adaptive rejection in the case of log-concavity or adaptive rejection Metropolis (Gilks et al., 1995) in the general case are particularly suitable for sampling in these situations.”
An application of Bayesian analysis and Markov chain Monte Carlo methods to the estimation of a regional trend in annual maxima (2006, methodology): “Consequently, the Metropolis-Hastings algorithm is likely to be a more suitable tool in this case, because of the possibility of using an asymmetrical jump distribution. Moreover, this example also emphasizes the importance of monitoring convergence of the chain, preferably by several methods, as su”
Slice sampling mixture models (2011, methodology): “If not, generic univariate simulation methods such as Adaptive Rejection Metropolis Sampling (Gilks et al. 1995) can be employed.”
Sample-Efficient Neural Architecture Search by Learning Action Space (2019, background): “This is efficient thanks to limited numbers of constraints [15], [16], [17].”
Non‐linear state space modelling of fisheries biomass dynamics by using Metropolis‐Hastings within‐Gibbs sampling (2000, methodology): “For this purpose we used adaptive rejection Metropolis sampling, ARMS (Gilks et al., 1995; Gilks and Neal, 1997).”
Modelling species diversity through species level hierarchical modelling (2005, methodology): “For all the remaining parameters we can use the adaptive rejection Metropolis sampling within Gibbs sampling that was introduced by Gilks et al. (1995).”