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Found 13 papers, 5 papers with code
A variational neural Bayes framework for inference on intractable posterior distributions
In this paper, we propose a framework for Bayesian posterior estimation by mapping data to posteriors of parameters using a neural network trained on data simulated from the complex model.
A Bayesian Semiparametric Method For Estimating Causal Quantile Effects
We adopt a semiparametric conditional distribution regression model that allows inference on any functionals of counterfactual distributions, including PDFs and multiple QTEs.
SPQR: An R Package for Semi-Parametric Density and Quantile Regression
We develop an R package SPQR that implements the semi-parametric quantile regression (SPQR) method in Xu and Reich (2021).
Kryging: Geostatistical analysis of large-scale datasets using Krylov subspace methods
Analyzing massive spatial datasets using Gaussian process model poses computational challenges.
Methodology Computation
Nonparametric Conditional Density Estimation In A Deep Learning Framework For Short-Term Forecasting
We propose a technique that simultaneously estimates the entire conditional distribution and flexibly allows for machine learning techniques to be incorporated.
DeepKriging: Spatially Dependent Deep Neural Networks for Spatial Prediction
Kriging provides the best linear unbiased predictor using covariance functions and is often associated with Gaussian processes.
Selecting Diverse Models for Scientific Insight
Model selection often aims to choose a single model, assuming that the form of the model is correct.
Global forensic geolocation with deep neural networks
An important problem in forensic analyses is identifying the provenance of materials at a crime scene, such as biological material on a piece of clothing.
Nearest-Neighbor Neural Networks for Geostatistics
We propose a more flexible spatial prediction method based on the Nearest-Neighbor Neural Network (4N) process that embeds deep learning into a geostatistical model.
A Spatial Bayesian Semiparametric Mixture Model for Positive Definite Matrices with Applications to Diffusion Tensor Imaging
Statistical analysis of DTI data is challenging because the data are positive definite matrices.
Methodology Applications
Deep Distribution Regression
Due to their flexibility and predictive performance, machine-learning based regression methods have become an important tool for predictive modeling and forecasting.
Bayesian Regression Using a Prior on the Model Fit: The R2-D2 Shrinkage Prior
The proposed method compares favourably to previous approaches in terms of both concentration around the origin and tail behavior, which leads to improved performance both in posterior contraction and in empirical performance.
Methodology
A spatial capture-recapture model for territorial species
Advances in field techniques have lead to an increase in spatially-referenced capture-recapture data to estimate a species' population size as well as other demographic parameters and patterns of space usage.
Applications
