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In general, the naive importance sampling (IS) estimator does not work well in examples involving simultaneous inference on several targets, because the importance weights can take arbitrarily large values, making the estimator...

Bayes factor Markov chain central limit theorem marginal likelihood polynomial ergodicity Reverse logistic regression /dk/atira/pure/subjectarea/asjc/2600/2613 name=Statistics and Probability /dk/atira/pure/subjectarea/asjc/1800/1804 name=Statistics, Probability and Uncertainty

In general, the naive importance sampling (IS) estimator does not work well in examples involving simultaneous inference on several targets, because the importance weights can take arbitrarily large values, making the estimator...

Bayes factor Markov chain central limit theorem marginal likelihood polynomial ergodicity Reverse logistic regression /dk/atira/pure/subjectarea/asjc/2600/2613 name=Statistics and Probability /dk/atira/pure/subjectarea/asjc/1800/1804 name=Statistics, Probability and Uncertainty