Results 31 to 40 of about 527,420 (298)
A Bayesian Nonparametric Learning Approach to Ensemble Models Using the Proper Bayesian Bootstrap
Algorithms, 2021 Bootstrap resampling techniques, introduced by Efron and Rubin, can be presented in a general Bayesian framework, approximating the statistical distribution of a statistical functional ϕ(F), where F is a random distribution function.
Marta Galvani +3 more
doaj +1 more source
A Survey on Bayesian Nonparametric Learning [PDF]
ACM Computing Surveys, 2019 Bayesian (machine) learning has been playing a significant role in machine learning for a long time due to its particular ability to embrace uncertainty, encode prior knowledge, and endow interpretability. On the back of Bayesian learning’s great success, Bayesian nonparametric learning (BNL) has emerged as a force for further advances in this field ...
Junyu Xuan +2 more
openaire +2 more sources
Sparse Bayesian nonparametric regression [PDF]
Proceedings of the 25th international conference on Machine learning - ICML '08, 2008 One of the most common problems in machine learning and statistics consists of estimating the mean response Xβ from a vector of observations y assuming y = Xβ + e where X is known, β is a vector of parameters of interest and e a vector of stochastic errors.
Caron, F, Doucet, A
openaire +2 more sources
Nonparametric Bayesian Data Analysis
Statistical Science, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Müller, Peter, 1963- +1 more
openaire +5 more sources
A tutorial on Bayesian nonparametric models [PDF]
Journal of Mathematical Psychology, 2012 A key problem in statistical modeling is model selection, how to choose a model at an appropriate level of complexity. This problem appears in many settings, most prominently in choosing the number ofclusters in mixture models or the number of factors in factor analysis.
Gershman, Samuel J., Blei, David M.
openaire +3 more sources
Nonparametric Full Bayesian Significance Testing for Bayesian Histograms
Physical Sciences ForumIn this article, we present an extension of the Full Bayesian Significance Test (FBST) for nonparametric settings, termed NP-FBST, which is constructed using the limit of finite dimension histograms. The test statistics for NP-FBST are based on a plug-in
Fernando Corrêa +2 more
doaj +1 more source
Robustness in Bayesian nonparametrics
International Journal of Approximate Reasoning, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +1 more source
Bayesian Nonparametric Weighted Sampling Inference
, 2021 It has historically been a challenge to perform Bayesian inference in a design-based survey context. The present paper develops a Bayesian model for sampling inference in the presence of inverse-probability weights.
Gelman, Andrew +2 more
core +1 more source
Applied Sciences, 2021 This paper addresses the problem of data vectors modeling, classification and recognition using infinite mixture models, which have been shown to be an effective alternative to finite mixtures in terms of selecting the optimal number of clusters. In this
Sami Bourouis +4 more
doaj +1 more source
Nonparametric Bayesian Regression
The Annals of Statistics, 1986 The paper addresses itself to Bayesian estimation of the function \[ F(x_ 1,x_ 2)=m+a(x_ 1)+b(x_ 2)+c(x_ 1,x_ 2) \] in the model \(y_ i=F(x_{1i},x_{2i})+e_ i\). A prior for F is constructed by putting independent priors on m,a,b, and c. They are normal distribution and Brownian motion.
openaire +3 more sources