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Persistent link: https://www.econbiz.de/10000878115
The computing time for Markov Chain Monte Carlo (MCMC) algorithms can be prohibitively large for datasets with many observations, especially when the data density for each observation is costly to evaluate. We propose a framework where the likelihood function is estimated from a random subset of...
Persistent link: https://www.econbiz.de/10010500806
We consider Bayesian inference by importance sampling when the likelihood is analytically intractable but can be unbiasedly estimated. We refer to this procedure as importance sampling squared (IS2), as we can often estimate the likelihood itself by importance sampling. We provide a formal...
Persistent link: https://www.econbiz.de/10013059994
Persistent link: https://www.econbiz.de/10011591626
The efficient importance sampling (EIS) method is a general principle for the numerical evaluation of high-dimensional integrals that uses the sequential structure of target integrands to build variance minimising importance samplers. Despite a number of successful applications in high...
Persistent link: https://www.econbiz.de/10014153378