Prior distributions for objective Bayesian analysis
We provide a review of prior distributions for objective Bayesian analysis. We start by
examining some foundational issues and then organize our exposition into priors for: i) …
examining some foundational issues and then organize our exposition into priors for: i) …
Bayesian hypothesis testing for Gaussian graphical models: Conditional independence and order constraints
DR Williams, J Mulder - Journal of Mathematical Psychology, 2020 - Elsevier
Gaussian graphical models (GGM; partial correlation networks) have become increasingly
popular in the social and behavioral sciences for studying conditional (in) dependencies …
popular in the social and behavioral sciences for studying conditional (in) dependencies …
Bayesian model comparison with the Hyvärinen score: Computation and consistency
The Bayes factor is a widely used criterion in model comparison and its logarithm is a
difference of out-of-sample predictive scores under the logarithmic scoring rule. However …
difference of out-of-sample predictive scores under the logarithmic scoring rule. However …
The semi-hierarchical Dirichlet process and its application to clustering homogeneous distributions
Assessing homogeneity of distributions is an old problem that has received considerable
attention, especially in the nonparametric Bayesian literature. To this effect, we propose the …
attention, especially in the nonparametric Bayesian literature. To this effect, we propose the …
A problem in forensic science highlighting the differences between the Bayes factor and likelihood ratio
DM Ommen, CP Saunders - Statistical Science, 2021 - projecteuclid.org
A Problem in Forensic Science Highlighting the Differences between the Bayes Factor and
Likelihood Ratio Page 1 Statistical Science 2021, Vol. 36, No. 3, 344–359 https://doi.org/10.1214/20-STS805 …
Likelihood Ratio Page 1 Statistical Science 2021, Vol. 36, No. 3, 344–359 https://doi.org/10.1214/20-STS805 …
On Bayes factors for hypothesis tests
We develop alternative families of Bayes factors for use in hypothesis tests as alternatives to
the popular default Bayes factors. The alternative Bayes factors are derived for the statistical …
the popular default Bayes factors. The alternative Bayes factors are derived for the statistical …
A short note on almost sure convergence of Bayes factors in the general set-up
D Chatterjee, T Maitra, S Bhattacharya - The American Statistician, 2020 - Taylor & Francis
Although there is a significant literature on the asymptotic theory of Bayes factor, the set-ups
considered are usually specialized and often involves independent and identically …
considered are usually specialized and often involves independent and identically …
A parsimonious tour of bayesian model uncertainty
PA Mattei - arXiv preprint arXiv:1902.05539, 2019 - arxiv.org
Modern statistical software and machine learning libraries are enabling semi-automated
statistical inference. Within this context, it appears easier and easier to try and fit many …
statistical inference. Within this context, it appears easier and easier to try and fit many …
Informed reversible jump algorithms
P Gagnon - Electronic Journal of Statistics, 2021 - projecteuclid.org
Incorporating information about the target distribution in proposal mechanisms generally
produces efficient Markov chain Monte Carlo algorithms (or at least, algorithms that are more …
produces efficient Markov chain Monte Carlo algorithms (or at least, algorithms that are more …
Specification uncertainty in modeling internet adoption: A developing city case analysis
A Ramírez-Hassan, DA Carvajal-Rendón - Utilities Policy, 2021 - Elsevier
Internet adoption fosters economic growth and development. Specifying policy control
drivers is particularly relevant for developing countries. However, there is no consensus on …
drivers is particularly relevant for developing countries. However, there is no consensus on …