Bayesian statistics without tears: A sampling-resampling perspective

By Adrian F. M. Smith, Alan E. Gelfand. 1992

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Citations in the corpus

A UNIFIED FRAMEWORK FOR MODELLING WILDLIFE POPULATION DYNAMICS † (2005, methodology): “…degree of perturbation is controlled by a smoothing parameter, a, where a = 0 corresponds to no smoothing (i.e. the non-parametric weighted bootstrap of Smith & Gelfand, 1992) and a = 1 corresponds to a parametric weighted bootstrap, where the parametric form is determined by the choice of kernel.”
Bayesian Methods in Conservation Biology (2000, methodology): “Examples include the sampling-importance-resampling method (Rubin 1988; Smith & Gelfand 1992) and the Markov chain Monte Carlo (Geyer 1992).”
Bayesian Updating with Structural Reliability Methods (2015, methodology): “Note that this algorithm is equivalent to a classical rejection sampling where the prior distribution is applied as an envelope distribution and the likelihood as a filter (Smith and Gelfand 1992).”
Inferences on Stress-Strength Reliability from Lindley Distributions (2013, methodology): “Here we are finding the percentiles of the weighted distribution of R, see Smith and Gelfand (1992).”
A hypothesis‐driven approach to optimize field campaigns (2012, background): “This holds, in particular, for all conditional distributions (via Bayesian updating [e.g., Smith and Gelfand, 1992]) of parameters and model predictions that arise when conditioning on hypothetical data from proposed designs, often calling for (geo)statistical inversion tools.”
Bayesian updating via bootstrap filtering combined with data-driven polynomial chaos expansions: methodology and application to history matching for carbon dioxide storage in geological formations (2013, methodology): “Technically, the conditional ensemble is obtained by simple rejection sampling [77] applied to an ensemble of parameter vectors drawn from the prior PDF, i.”
Local Adaptive Importance Sampling for Multivariate Densities with Strong Nonlinear Relationships (1996, methodology): “The effectiveness of the SIR algorithm can be strongly influenced by the quality of the envelope (Rubin, 1987, 1988; Smith et al., 1987; Geweke, 1989; Oh and Berger, 1991; Smith and Gelfand, 1992; Hesterberg, 1991; Stewart, 198:3: Givens.”
Sequential Monte Carlo Methods in Practice (2001, methodology): “A natural way of recursively generating such samples is to specify a prior distribution from which a sample can be simulated, and then to use weighted bootstrap techniques as described by (Smith and Gelfand 1992), with weights defined by the likelihood of the most recent data, to update that sample ”
Directional distance functions: Optimal endogenous directions (2016, methodology): “Here we use the weighted bootstrap method presented by Smith and Gelfand (1992), which is similar to the sampling-importance-resampling method of Rubin (1988).”