Supplemental material
Open access
11,136
Views
64
CrossRef citations to date
0
Altmetric
Bayesian Models
BAMLSS: Bayesian Additive Models for Location, Scale, and Shape (and Beyond)
Nikolaus UmlaufDepartment of Statistics, Universität Innsbruck, Innsbruck, AustriaCorrespondence[email protected]
, Nadja KleinUniversity of Melbourne, Melbourne Business School, Melbourne, Australia
& Achim ZeileisDepartment of Statistics, Universität Innsbruck, Innsbruck, Austria
Pages 612-627
|
Received 22 Feb 2017, Published online: 14 Jun 2018
Related Research Data
On the Half-Cauchy Prior for a Global Scale Parameter
Source:
Institute of Mathematical Statistics
Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models
Source:
Informa UK Limited
Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression
Source:
International Society for Bayesian Analysis
Why Does It Always Rain on Me? A Spatio-Temporal Analysis of Precipitation in Austria
Source:
Austrian Statistical Society
Partitioned algorithms for maximum likelihood and other non-linear estimation
Source:
Springer Nature
A Mixed Model Approach for Geoadditive Hazard Regression
Source:
Wiley-Blackwell
Stan : A Probabilistic Programming Language
Source:
Foundation for Open Access Statistics
Multilevel structured additive regression
Source:
Springer Science and Business Media LLC
Bayesian Generalized Additive Models for Location, Scale, and Shape for Zero-Inflated and Overdispersed Count Data
Source:
Informa UK Limited
Simultaneous selection of variables and smoothing parameters in structured additive regression models
Source:
Elsevier BV
Bayesian structured additive distributional regression with an application to regional income inequality in Germany
Source:
Institute of Mathematical Statistics
Bayesian Smoothing with Gaussian Processes Using Fourier Basis Functions in thespectralGPPackage
Source:
Foundation for Open Access Statistic
Slice sampling
Source:
Institute of Mathematical Statistics
On Gibbs sampling for state space models
Source:
Oxford University Press (OUP)
WinBUGS – A Bayesian modelling framework: Concepts, structure, and extensibility
Source:
Springer Science and Business Media LLC
On Gibbs sampling for state space models
Source:
Oxford University Press (OUP)
Related research
People also read lists articles that other readers of this article have read.
Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.
Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.