21 research outputs found
Bernstein - von Mises theorem and misspecified models: a review
This is a review of asymptotic and non-asymptotic behaviour of Bayesian
methods under model specification. In particular we focus on consistency, i.e.
convergence of the posterior distribution to the point mass at the best
parametric approximation to the true model, and conditions for it to be locally
Gaussian around this point. For well specified regular models, variance of the
Gaussian approximation coincides with the Fisher information, making Bayesian
inference asymptotically efficient. In this review, we discuss how this is
affected by model misspecification. We also discuss approaches to adjust
Bayesian inference to make it asymptotically efficient under model
misspecification
Spike and slab variational Bayes for high dimensional logistic regression
No abstract availabl
Fitting latent non-Gaussian models using variational Bayes and Laplace approximations
Latent Gaussian models (LGMs) are perhaps the most commonly used class of
models in statistical applications. Nevertheless, in areas ranging from
longitudinal studies in biostatistics to geostatistics, it is easy to find
datasets that contain inherently non-Gaussian features, such as sudden jumps or
spikes, that adversely affect the inferences and predictions made from an LGM.
These datasets require more general latent non-Gaussian models (LnGMs) that can
handle these non-Gaussian features automatically. However, fast implementation
and easy-to-use software are lacking, which prevent LnGMs from becoming widely
applicable. In this paper, we derive variational Bayes algorithms for fast and
scalable inference of LnGMs. The approximation leads to an LGM that downweights
extreme events in the latent process, reducing their impact and leading to more
robust inferences. It can be applied to a wide range of models, such as
autoregressive processes for time series, simultaneous autoregressive models
for areal data, and spatial Mat\'ern models. To facilitate Bayesian inference,
we introduce the ngvb package, where LGMs implemented in R-INLA can be easily
extended to LnGMs by adding a single line of code.Comment: 30 pages, 10 figure