A generalized Tweedie identity and moment-generating-function representation enable nonparametric recovery of full posteriors for heteroscedastic normal means with unknown variances without specifying a prior.
Proceedings of the Royal Society of London , volume=
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BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.
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Nonparametric f-Modeling for Empirical Bayes Inference with Unequal and Unknown Variances
A generalized Tweedie identity and moment-generating-function representation enable nonparametric recovery of full posteriors for heteroscedastic normal means with unknown variances without specifying a prior.
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BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.