Results 81 to 90 of about 527,420 (298)
Computational Light Field Generation Using Deep Nonparametric Bayesian Learning
IEEE Access, 2019 In this paper, we present a deep nonparametric Bayesian method to synthesize a light field from a single image. Conventionally, light-field capture requires special optical architecture, and the gain in angular resolution often comes at the expense of a ...
Nan Meng +3 more
doaj +1 more source
Laminar Burning Velocity of Ammonia‐Hydrogen Mixtures Predicted by Machine Learning
Chemie Ingenieur Technik, EarlyView.Ammonia–hydrogen blends aid defossilization, but slow combustion kinetics require complex modeling. Although chemical kinetic modeling is computationally expensive, machine learning surrogates offer fast predictions. This study trained GPR, DT, and ANN models on 1445 experimental points.
Georg Klepp +2 more
wiley +1 more source
Bayesian Nonparametric Covariance Regression
J. Mach. Learn. Res., 2011 33 pages, 8 ...
Emily B. Fox, David B. Dunson
openaire +3 more sources
Bayesian Nonparametrics for Sparse Dynamic Networks
, 2023 In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks. A positive parameter is associated to each node of a network, which models the sociability of that node. Sociabilities are assumed to evolve over time, and are modelled via a dynamic point process model. The model is able to capture long term evolution
Cian Naik +4 more
openaire +4 more sources
Bayesian Nonparametric Quantile Process Regression
, 2021 Codes to reproduce QUINN, a novel Bayesian nonparametric quantile process ...
Steven Guanxing Xu
core +1 more source
The Canadian Journal of Chemical Engineering, EarlyView.Abstract This study investigates the effect of moisture on CO2 adsorption in South African coals using both experimental and machine learning approaches. Three coal samples (SL, TN, and EM) with varying ranks (RoVmr: 3.49%, 1.26%, and 0.64%, respectively) were collected from different regions of South Africa.
Kasturie Premlall +3 more
wiley +1 more source
A Bayesian nonparametric approach to testing for dependence between random variables [PDF]
, 2016 Nonparametric and nonlinear measures of statistical dependence between pairs of random variables are important tools in modern data analysis. In particular the emergence of large data sets can now support the relaxation of linearity assumptions implicit ...
Filippi, Sarah +3 more
core +1 more source
The Canadian Journal of Chemical Engineering, EarlyView.This study shows that integrating multiple machine learning models with optimization and decision‐making improves chemical process design, and that a consensus‐based strategy across models provides more robust and reliable operating recommendations than any single model, especially under limited or noisy data conditions.
Farough Agin +2 more
wiley +1 more source
Change-point problems: a Bayesian nonparametric approach [PDF]
, 1985 summary:A change-point problem is examined from a Bayesian viewpoint, under nonparametric hypotheses. A Ferguson-Dirichlet prior is chosen and the posterior distribution is computed for the change-point and for the unknown distribution ...
M. Scarsini +3 more
core +1 more source
Copula‐based joint modelling of emergency department visits with time‐varying dependence
Canadian Journal of Statistics, EarlyView.Abstract Jointly modelling multiple correlated count time series is essential in health services research, where outcomes like emergency visits for mental health and substance use often evolve together. Ignoring these dependencies can obscure meaningful trends and limit the effectiveness of policy evaluation.
Guanjie Lyu, Cindy Feng, Lihui Liu
wiley +1 more source