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Related papers: Intuitive joint priors for variance parameters

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Advancements in computational power and methodologies have enabled research on massive datasets. However, tools for analyzing data with directional or periodic characteristics, such as wind directions and customers' arrival time in 24-hour…

Methodology · Statistics 2026-04-07 Xiang Ye , Janet Van Niekerk , Håvard Rue

Deep learning has revolutionized the last decade, being at the forefront of extraordinary advances in a wide range of tasks including computer vision, natural language processing, and reinforcement learning, to name but a few. However, it…

Machine Learning · Computer Science 2024-01-24 Sebastian W. Ober

Feature selection is an important but challenging task in causal inference for obtaining unbiased estimates of causal quantities. Properly selected features in causal inference not only significantly reduce the time required to implement a…

Methodology · Statistics 2025-02-04 Tianyu Yang , Md. Noor-E-Alam

Explainable AI aims to overcome the black-box nature of complex ML models like neural networks by generating explanations for their predictions. Explanations often take the form of a heatmap identifying input features (e.g. pixels) that are…

Machine Learning · Computer Science 2024-04-16 Pattarawat Chormai , Jan Herrmann , Klaus-Robert Müller , Grégoire Montavon

Predictive recursion is an accurate and computationally efficient algorithm for nonparametric estimation of mixing densities in mixture models. In semiparametric mixture models, however, the algorithm fails to account for any uncertainty in…

Methodology · Statistics 2015-03-19 Ryan Martin , Surya T. Tokdar

We provide a new characterization of the Dirichlet distribution. This characterization implies that under assumptions made by several previous authors for learning belief networks, a Dirichlet prior on the parameters is inevitable.

Artificial Intelligence · Computer Science 2013-02-21 Dan Geiger , David Heckerman

Forecast combination integrates information from various sources by consolidating multiple forecast results from the target time series. Instead of the need to select a single optimal forecasting model, this paper introduces a deep learning…

Machine Learning · Computer Science 2023-11-27 Yinuo Ren , Feng Li , Yanfei Kang , Jue Wang

Variational inference is a popular method for estimating model parameters and conditional distributions in hierarchical and mixed models, which arise frequently in many settings in the health, social, and biological sciences. Variational…

Methodology · Statistics 2019-01-10 Ted Westling , Tyler H. McCormick

We combine Bayesian prediction and weighted inference as a unified approach to survey inference. The general principles of Bayesian analysis imply that models for survey outcomes should be conditional on all variables that affect the…

Methodology · Statistics 2020-06-24 Yajuan Si , Rob Trangucci , Jonah Sol Gabry , Andrew Gelman

Modeling uncertainty in deep neural networks, despite recent important advances, is still an open problem. Bayesian neural networks are a powerful solution, where the prior over network weights is a design choice, often a normal…

Machine Learning · Statistics 2019-10-29 Raanan Y. Rohekar , Yaniv Gurwicz , Shami Nisimov , Gal Novik

Mixtures of Zellner's g-priors have been studied extensively in linear models and have been shown to have numerous desirable properties for Bayesian variable selection and model averaging. Several extensions of g-priors to Generalized…

Methodology · Statistics 2018-05-08 Yingbo Li , Merlise A. Clyde

Shrinkage priors are a popular Bayesian paradigm to handle sparsity in high-dimensional regression. Still limited, however, is a flexible class of shrinkage priors to handle grouped sparsity, where covariates exhibit some natural grouping…

Methodology · Statistics 2025-12-16 Eric Yanchenko , Kaoru Irie , Shonosuke Sugasawa

Probabilistic sensitivity analysis identifies the influential uncertain input to guide decision-making. We propose a general sensitivity framework with respect to the input distribution parameters that unifies a wide range of sensitivity…

Methodology · Statistics 2023-02-10 Jiannan Yang

We introduce the Group-R2 decomposition prior, a hierarchical shrinkage prior that extends R2-based priors to structured regression settings with known groups of predictors. By decomposing the prior distribution of the coefficient of…

Methodology · Statistics 2025-07-28 Javier Enrique Aguilar , David Kohns , Aki Vehtari , Paul-Christian Bürkner

Post-treatment confounding is a common problem in causal inference, including special cases of noncompliance, truncation by death, surrogate endpoint, etc. Principal stratification (Frangakis and Rubin 2002) is a general framework for…

Computation · Statistics 2023-04-07 Bo Liu , Fan Li

The inference of deep hierarchical models is problematic due to strong dependencies between the hierarchies. We investigate a specific transformation of the model parameters based on the multivariate distributional transform. This…

Machine Learning · Statistics 2018-12-12 Jakob Knollmüller , Torsten A. Enßlin

Encoding domain knowledge into the prior over the high-dimensional weight space of a neural network is challenging but essential in applications with limited data and weak signals. Two types of domain knowledge are commonly available in…

Machine Learning · Statistics 2023-03-31 Tianyu Cui , Aki Havulinna , Pekka Marttinen , Samuel Kaski

The interpretation of data in terms of multi-parameter models of new physics, using the Bayesian approach, requires the construction of multi-parameter priors. We propose a construction that uses elements of Bayesian reference analysis. Our…

Data Analysis, Statistics and Probability · Physics 2011-08-03 Maurizio Pierini , Harrison B. Prosper , Sezen Sekmen , Maria Spiropulu

In this work, the uncertainty associated with the finite element discretization error is modeled following the Bayesian paradigm. First, a continuous formulation is derived, where a Gaussian process prior over the solution space is updated…

Numerical Analysis · Mathematics 2024-03-11 Anne Poot , Pierre Kerfriden , Iuri Rocha , Frans van der Meer

We consider universal inference in variance components models, focusing on settings where the parameter is near or at the boundary of the parameter set. Two cases, which are not handled by existing state-of-the-art methods, are of…

Methodology · Statistics 2025-09-03 Yiqiao Zhang , Karl Oskar Ekvall , Aaron J. Molstad
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