Assigning a value to a power likelihood in a general Bayesian model
By Christopher C. Holmes, Stephen G. Walker. 2017
163 citations recorded by the source corpus.
Bayesian approaches to data analysis and machine learning are widespread and popular as they provide intuitive yet rigorous axioms for learning from data; see Bernardo and Smith (2004) and Bishop (2006). However, this rigour comes with a caveat that the Bayesian model is a precise reflection of Nature. There has been a recent trend to address potential model misspecification by raising the likelihood function to a power, primarily for robustness reasons, though not exclusively. In this paper we provide a coherent specification of the power parameter once the Bayesian model has been specified in the absence of a perfect model.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/9f5ccbe7529a63c61daaabc1174ec499bfe6967d
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Citations in the corpus
- Robust generalised Bayesian inference for intractable likelihoods (2021, unknown): “…(Berger et al., 1994), the so-called safe Bayes approach (Grünwald, 2011, 2012; Grünwald & van Ommen, 2017; de Heide et al., 2020), power posteriors (e.g. Holmes & Walker, 2017), coarsened posteriors (Miller & Dunson, 2019), bagged posteriors (Huggins & Miller, 2020), 𝜌 - posteriors (Baraud &…”
- Calibrating general posterior credible regions (2015, background): “scaling posterior distributions to address model misspeci- cation have been considered recently in the literature. Ideas for choosing the posterior scaling are presented in Bissiri et al. (2016) and Holmes and Walker (2017), including hierarchical Bayes and loss/information matching, and a novel ide”