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The proposal and study of dependent prior processes has been a major research focus in the recent Bayesian nonparametric literature. In this paper, we introduce a flexible class of dependent nonparametric priors, investigate their…

Statistics Theory · Mathematics 2014-07-03 Antonio Lijoi , Bernardo Nipoti , Igor Prünster

We consider a prior for nonparametric Bayesian estimation which uses finite random series with a random number of terms. The prior is constructed through distributions on the number of basis functions and the associated coefficients. We…

Statistics Theory · Mathematics 2015-02-10 Weining Shen , Subhashis Ghosal

Bayesian field theory denotes a nonparametric Bayesian approach for learning functions from observational data. Based on the principles of Bayesian statistics, a particular Bayesian field theory is defined by combining two models: a…

Data Analysis, Statistics and Probability · Physics 2007-05-23 J. C. Lemm

We review common situations in Bayesian latent variable models where the prior distribution that a researcher specifies differs from the prior distribution used during estimation. These situations can arise from the positive definite…

Methodology · Statistics 2024-11-19 Edgar C. Merkle , Oludare Ariyo , Sonja D. Winter , Mauricio Garnier-Villarreal

Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for an observation matrix with p features across n samples. We develop a structured Bayesian group factor analysis model…

Methodology · Statistics 2015-11-12 Shiwen Zhao , Chuan Gao , Sayan Mukherjee , Barbara E Engelhardt

We present a computational framework for efficient learning, sampling, and distribution of general Bayesian posterior distributions. The framework leverages a machine learning approach for the construction of normalizing flows for the…

Nuclear Theory · Physics 2023-10-10 Yukari Yamauchi , Landon Buskirk , Pablo Giuliani , Kyle Godbey

Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights. Recent work developing this class of methods has explored ever richer parameterizations of the…

We consider heteroscedastic nonparametric regression models, when both the mean function and variance function are unknown and to be estimated with nonparametric approaches. We derive convergence rates of posterior distributions for this…

Statistics Theory · Mathematics 2010-10-07 Yuao Hu

Which neural networks are similar is a fundamental question for both machine learning and neuroscience. Here, it is proposed to base comparisons on the predictive distributions of linear readouts from intermediate representations. In…

Machine Learning · Computer Science 2025-05-27 Heiko H. Schütt

In this paper, we introduce a probabilistic model for learning nonnegative matrix factorization (NMF) that is commonly used for predicting missing values and finding hidden patterns in the data, in which the matrix factors are latent…

Machine Learning · Computer Science 2022-06-22 Jun Lu , Xuanyu Ye

Bayesian learning is a powerful learning framework which combines the external information of the data (background information) with the internal information (training data) in a logically consistent way in inference and prediction. By…

Machine Learning · Statistics 2026-02-11 Erdong Guo , David Draper

Computer experiments are becoming increasingly important in scientific investigations. In the presence of uncertainty, analysts employ probabilistic sensitivity methods to identify the key-drivers of change in the quantities of interest.…

Methodology · Statistics 2024-07-02 Isadora Antoniano-Villalobos , Emanuele Borgonovo , Xuefei Lu

We introduce a Bayesian perspective for the structured matrix factorization problem. The proposed framework provides a probabilistic interpretation for existing geometric methods based on determinant minimization. We model input data…

Machine Learning · Computer Science 2023-02-17 Gokcan Tatli , Alper T. Erdogan

Bayesian methods are actively used for parameter identification and uncertainty quantification when solving nonlinear inverse problems with random noise. However, there are only few theoretical results justifying the Bayesian approach.…

Statistics Theory · Mathematics 2020-02-04 Vladimir Spokoiny

Motivated by parametric models for which the likelihood is analytically unavailable, numerically unstable, or prohibitively expensive to compute or optimize, we develop a prior- and likelihood-free framework for fully probabilistic…

Methodology · Statistics 2026-03-17 Leonardo Cella , Emily C. Hector

Like mean, quantile and variance, mode is also an important measure of central tendency and data summary. Many practical questions often focus on "Which element (gene or file or signal) occurs most often or is the most typical among all…

Methodology · Statistics 2012-08-03 Keming Yu , Katerina Aristodemou

One of the main research areas in Bayesian Nonparametrics is the proposal and study of priors which generalize the Dirichlet process. Here we exploit theoretical properties of Poisson random measures in order to provide a comprehensive…

Statistics Theory · Mathematics 2007-06-13 Lancelot F. James , Antonio Lijoi , Igor Pruenster

Optimization is widely used in statistics, and often efficiently delivers point estimates on useful spaces involving structural constraints or combinatorial structure. To quantify uncertainty, Gibbs posterior exponentiates the negative loss…

Methodology · Statistics 2025-07-23 Cheng Zeng , Eleni Dilma , Jason Xu , Leo L Duan

Bayesian estimation is a powerful theoretical paradigm for the operation of quantum sensors. However, the Bayesian method for statistical inference generally suffers from demanding calibration requirements that have so far restricted its…

Quantum Physics · Physics 2021-09-22 Samuel P. Nolan , Augusto Smerzi , Luca Pezzè

We consider a binary unsupervised classification problem where each observation is associated with an unobserved label that we want to retrieve. More precisely, we assume that there are two groups of observation: normal and abnormal. The…

Machine Learning · Statistics 2011-05-05 Stevenn Volant , Marie-Laure Martin Magniette , Stéphane Robin