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stat.CO2019
Robustly estimating the marginal likelihood for cognitive models via importance sampling
Minh-Ngoc Tran, Marcel Scharth, David Gunawan +3
Recent advances in Markov chain Monte Carlo (MCMC) extend the scope of Bayesian inference to models for which the likelihood function is intractable. Although these developments al…
stat.CO2015★ 2 cited
Markov Interacting Importance Samplers
Eduardo F. Mendes, Marcel Scharth, Robert Kohn
We introduce a new Markov chain Monte Carlo (MCMC) sampler called the Markov Interacting Importance Sampler (MIIS). The MIIS sampler uses conditional importance sampling (IS) appro…