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sparse graphs, because they allow a faster learning rate, relative to ordinary local priors, when the true unknown sampling …
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importance sampling or the independence chain Metropolis-Hastings algorithm for posterior analysis. A comparative analysis is … appropriately yet quickly tuned candidate, straightforward importance sampling provides the most efficient estimator of the marginal …
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We analyze the general (multiallelic) Hardy-Weinberg equilibrium problem from an objective Bayesian testing standpoint. We argue that for small or moderate sample sizes the answer is rather sensitive to the prior chosen, and this suggests to carry out a sensitivity analysis with respect to the...
Persistent link: https://www.econbiz.de/10010343881
quickly tuned adaptive candidate, straightforward importance sampling provides a computationally efficient estimator of the …
Persistent link: https://www.econbiz.de/10011380802
The implementation of the Bayesian paradigm to model comparison can be problematic. In particular, prior distributions on the parameter space of each candidate model require special care. While it is well known that improper priors cannot be used routinely for Bayesian model comparison, we claim...
Persistent link: https://www.econbiz.de/10010343918
Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC) methods. Not only do SMC...
Persistent link: https://www.econbiz.de/10011504888
discriminatory power against sampling distributions close to the smaller model. However, this drawback becomes rapidly negligible as …
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