Markov Chain Monte Carlo in Practice

By Walter R. Gilks, Sylvia Richardson, David J. Spiegelhalter. 1997

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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, methodology): “The product of the CPOs, called the pseudo-marginal likelihood (PsML), has been proposed as a proxy for the marginal likelihood f (y) (Gelfand and Dey, 1994; Gelfand, 1996).”
Beyond Data and Model Parallelism for Deep Neural Networks (2018, methodology): “a technique for obtaining samples from a probability distribution so that higher probability samples are visited proportionately more often than low probability samples. A common method (described in [17]) to transform a cost function cost() into a probability distribution is the following, where is”
An Adaptive Sequential Monte Carlo Sampler (2010, unknown): “Propose a move to a new location, ˜ θ , by drawing a sample from q h ( θ ( i − 1) , ˜ θ ).”
Accounting for input-model and input-parameter uncertainties in simulation (2004, methodology): “The authors thank Russell R. Barton, Bibhuti B. Bhattacharyya, Russell C. H. Cheng, Stephen E. Chick, Sujit K. Ghosh, and Stephen D. Roberts for their enlightening discussions on this paper.”
Conditional random fields for activity recognition (2007, methodology): “With computational limitations in mind, we prefer models with structures that permit exact inference over more complex models that require approximate inference, such as loopy belief propagation [75] or Markov chain Monte Carlo [31], because inference in more complex models tends to require more com”