Search Results for author:
Found 13 papers, 9 papers with code
Treatment effect estimation with Multilevel Regression and Poststratification
Using MRP-style estimators, treatment effect estimates for areas as small as 1. 3$\%$ of the population have lower bias and variance than standard causal inference methods, even in the presence of treatment effect heterogeneity.
Causal Inference
Methodology
Improving multilevel regression and poststratification with structured priors
A central theme in the field of survey statistics is estimating population-level quantities through data coming from potentially non-representative samples of the population.
Methodology
The Integrated Nested Laplace Approximation for fitting Dirichlet regression models
This paper introduces a Laplace approximation to Bayesian inference in Dirichlet regression models, which can be used to analyze a set of variables on a simplex exhibiting skewness and heteroscedasticity, without having to transform the data.
Bayesian Inference
Computation
Methodology
Rank-normalization, folding, and localization: An improved $\widehat{R}$ for assessing convergence of MCMC
In this paper we show that the convergence diagnostic $\widehat{R}$ of Gelman and Rubin (1992) has serious flaws.
Computation Methodology
Validating Bayesian Inference Algorithms with Simulation-Based Calibration
Verifying the correctness of Bayesian computation is challenging.
Methodology
Yes, but Did It Work?: Evaluating Variational Inference
While it's always possible to compute a variational approximation to a posterior distribution, it can be difficult to discover problems with this approximation.
Visualization in Bayesian workflow
Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains.
Methodology Applications
Using stacking to average Bayesian predictive distributions
The widely recommended procedure of Bayesian model averaging is flawed in the M-open setting in which the true data-generating process is not one of the candidate models being fit.
Methodology Computation
Asynchronous Gibbs Sampling
We introduce a theoretical framework for analyzing asynchronous Gibbs sampling and other extensions of MCMC that do not possess the Markov property.
Computation
Pareto Smoothed Importance Sampling
Importance weighting is a general way to adjust Monte Carlo integration to account for draws from the wrong distribution, but the resulting estimate can be highly variable when the importance ratios have a heavy right tail.
Does non-stationary spatial data always require non-stationary random fields?
A stationary spatial model is an idealization and we expect that the true dependence structures of physical phenomena are spatially varying, but how should we handle this non-stationarity in practice?
Methodology Applications
On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoods
The methodology is reviewed on well-known examples such as the parameters in Ising models, the posterior for Fisher-Bingham distributions on the $d$-Sphere and a large-scale Gaussian Markov Random Field model describing the Ozone Column data.
Exploring a New Class of Non-stationary Spatial Gaussian Random Fields with Varying Local Anisotropy
This allows for the introduction of parameters that control the GRF by parametrizing the diffusion matrix.
Methodology
