Density Estimation via Bayesian Inference Engines

DOI10.48550/arXiv.2009.06182zbMath1490.62096arXiv2009.06182OpenAlexW3210091022MaRDI QIDQ59197

M. P. Wand, J. C. F. Yu, J. C. F. Yu, Matthew P. Wand

Publication date: 14 September 2020

Published in: AStA. Advances in Statistical Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2009.06182

zbMATH Keywords

slice samplingexpectation propagationmixed model-based penalized splinesno-U-turn samplersemiparametric mean field variational Bayes

Mathematics Subject Classification ID

Density estimation (62G07) Bayesian inference (62F15)

Related Items

densEstBayes

Uses Software

• R
• KernSmooth
• SemiPar
• BUGS
• Infer.NET
• JAGS
• bnsoft
• Stan
• Blaise
• NUTS

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
• Variational inference for count response semiparametric regression
• Kernel density estimation via diffusion
• Extent to which least-squares cross-validation minimises integrated square error in nonparametric density estimation
• On confidence bands in nonparametric density estimation and regression
• Exact mean integrated squared error
• Interpolating spline methods for density estimation. I: Equi-spaced knots
• Slice sampling. (With discussions and rejoinder)
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• Simultaneous confidence bands for linear regression and smoothing
• On the accuracy of binned kernel density estimators
• Semiparametric regression during 2003--2007
• Confidence bands in density estimation
• ON SEMIPARAMETRIC REGRESSION WITH O'SULLIVAN PENALIZED SPLINES
• Graphical Models, Exponential Families, and Variational Inference
• Empirical likelihood confidence intervals for nonparametric density estimation
• Density Estimation and Bump-Hunting by the Penalized Likelihood Method Exemplified by Scattering and Meteorite Data
• Bayesian density estimation using bernstein polynomials
• Semiparametric Regression with R
• On expectation propagation for generalised, linear and mixed models
• Bayesian Density Estimation and Inference Using Mixtures
• Discrete Multivariate Analysis Theory and Practice