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Increasingly complex datasets pose a number of challenges for Bayesian inference. Conventional posterior sampling based on Markov chain Monte Carlo can be too computationally intensive, is serial in nature and mixes poorly between posterior modes. Fu
Externí odkaz:
http://arxiv.org/abs/1902.03175
In this paper we revisit the weighted likelihood bootstrap, a method that generates samples from an approximate Bayesian posterior of a parametric model. We show that the same method can be derived, without approximation, under a Bayesian nonparametr
Externí odkaz:
http://arxiv.org/abs/1709.07616
Autor:
Lyddon, Simon
Publikováno v:
Veterinary Record: Journal of the British Veterinary Association; 6/5/2021, Vol. 188 Issue 11, piii-iii, 1p
