Results 31 to 40 of about 15,452 (256)

Variational methods for the Dirichlet process [PDF]

Twenty-first international conference on Machine learning - ICML '04, 2004
Variational inference methods, including mean field methods and loopy belief propagation, have been widely used for approximate probabilistic inference in graphical models. While often less accurate than MCMC, variational methods provide a fast deterministic approximation to marginal and conditional probabilities.
David M. Blei, Michael I. Jordan
openaire   +2 more sources

Spiked Dirichlet Process Priors for Gaussian Process Models

Journal of Probability and Statistics, 2010
We expand a framework for Bayesian variable selection for Gaussian process (GP) models by employing spiked Dirichlet process (DP) prior constructions over set partitions containing covariates.
Terrance Savitsky, Marina Vannucci
doaj   +1 more source

Modeling geo-homopholy in online social networks for population distribution projection [PDF]

International Journal of Crowd Science, 2017
Purpose – Projecting the population distribution in geographical regions is important for many applications such as launching marketing campaigns or enhancing the public safety in certain densely populated areas.
Yuanxing Zhang, Zhuqi Li, Kaigui Bian, Yichong Bai, Zhi Yang, Xiaoming Li   +5 more
doaj   +1 more source

Efficient Clustering for Continuous Occupancy Mapping Using a Mixture of Gaussian Processes

Sensors, 2022
This paper proposes a novel method for occupancy map building using a mixture of Gaussian processes. Gaussian processes have proven to be highly flexible and accurate for a robotic occupancy mapping problem, yet the high computational complexity has been
Soohwan Kim, Jonghyuk Kim
doaj   +1 more source

Robust Bayesian Estimation of Mixed Normal Dirichlet Models to Study the Effect of Some Climatic Factors on Evaporation

Proceedings of the International Conference on Applied Innovations in IT
This study proposes and validates a robust Bayesian model based on a Dirichlet process mixture of normals (DMNM) for probability density estimation and missing data imputation in multivariate datasets.
Hassan Sami, Asmaa Ayoob
doaj   +1 more source

Models beyond the Dirichlet process [PDF]

SSRN Electronic Journal, 2009
Bayesian nonparametric inference is a relatively young area of research and it has recently undergone a strong development. Most of its success can be explained by the considerable degree of flexibility it ensures in statistical modeling, if compared to parametric alternatives, and by the emergence of new and efficient simulation techniques that make ...
LIJOI, ANTONIO, PRUENSTER, IGOR
openaire   +9 more sources

The dynamic hierarchical Dirichlet process [PDF]

Proceedings of the 25th international conference on Machine learning - ICML '08, 2008
The dynamic hierarchical Dirichlet process (dHDP) is developed to model the time-evolving statistical properties of sequential data sets. The data collected at any time point are represented via a mixture associated with an appropriate underlying model, in the framework of HDP. The statistical properties of data collected at consecutive time points are
Lu Ren, David B. Dunson, Lawrence Carin
openaire   +2 more sources

Prior Design for Dependent Dirichlet Processes: An Application to Marathon Modeling. [PDF]

PLoS ONE, 2016
This paper presents a novel application of Bayesian nonparametrics (BNP) for marathon data modeling. We make use of two well-known BNP priors, the single-p dependent Dirichlet process and the hierarchical Dirichlet process, in order to address two ...
Melanie F Pradier, Francisco J R Ruiz, Fernando Perez-Cruz   +2 more
doaj   +1 more source

Influence of Scan Strategies in Electron Beam Powder Bed Fusion on Solidification, Microstructure, and High‐Temperature Compressive Properties of γ′‐Strengthened Inconel 738LC

Advanced Engineering Materials, EarlyView.
Experiments and thermophysical simulations were conducted to investigate the electron beam powder bed fusion electron beam (PBF‐EB/M) process for the γ′‐strengthened nickel‐based superalloy Inconel 738LC. The results demonstrate the impact of process‐induced microstructural variations on high‐temperature mechanical behavior, providing a basis for ...
Jan Niklas Petenati, Karthik Kumar Banku, Prasanth Bondi, Ivan Zhirnov, Yangyiwei Yang, Gordon Schmidt, Jürgen Christen, Georg Hasemann, Manja Krüger, Bai‐Xiang Xu, Bilal Gökce, Silja‐Katharina Rittinghaus   +11 more
wiley   +1 more source

Untangling sample and population level estimands in Bayesian causal computation

Journal of Causal Inference
Model-based Bayesian inference for sample and population-level causal estimands has been growing in popularity. This literature routinely emphasizes clear specification of the target estimand, however blind implementation of standard computational ...
Oganisian Arman
doaj   +1 more source