Results 121 to 130 of about 527,420 (298)
On consistency of nonparametric normal mixtures for Bayesian density estimation. [PDF]
The past decade has seen a remarkable development in the area of Bayesian nonparametric inference both from a theoretical and applied perspective. As for the latter, the celebrated Dirichlet process has been successfully exploited within Bayesian mixture
Antonio Lijoi +2 more
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Bayesian nonparametric hidden Markov models with application to the analysis of copy-number-variation in mammalian genomes [PDF]
, 2009 We consider the development of Bayesian Nonparametric methods for product partition models such as Hidden Markov Models and change point models.
Yau, C. +3 more
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Corporate Social Responsibility and Environmental Management, EarlyView.ABSTRACT Voluntary carbon offsetting programs (VCOPs) are available for airline passengers who want to take action and offset their scope 3 emissions. Yet, their uptake is very limited. In this study, we propose an integrated theoretical model involving the norm activation theory and protection motivation theory to understand airline passengers ...
Eric Tchouamou Njoya +4 more
wiley +1 more source
Nonparametric Bayesian Semi-supervised Word Segmentation
Transactions of the Association for Computational Linguistics, 2021 Ryo Fujii, Ryo Domoto, Daichi Mochihashi
doaj +1 more source
A nonparametric Bayesian basket trial design
Biometrical Journal, 2018 AbstractTargeted therapies on the basis of genomic aberrations analysis of the tumor have shown promising results in cancer prognosis and treatment. Regardless of tumor type, trials that match patients to targeted therapies for their particular genomic aberrations have become a mainstream direction of therapeutic management of patients with cancer ...
Yanxun Xu +3 more
openaire +4 more sources
Asymptotics for a Bayesian nonparametric estimator of species richness [PDF]
In Bayesian nonparametric inference, random discrete probability measures are commonly used as priors within hierarchical mixture models for density estimation and for inference on the clustering of the data. Recently it has been shown that they can also
Stefano Favaro +2 more
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Bayesian Semiparametric Multi-State Models [PDF]
, 2006 Multi-state models provide a unified framework for the description of the evolution of discrete phenomena in continuous time. One particular example are Markov processes which can be characterised by a set of time-constant transition intensities between ...
Kneib, Thomas, Hennerfeind, Andrea
core +1 more source
Deep Underground Science and Engineering, EarlyView.Probabilistic natural gradient boosting and Gaussian process regression models accurately predict rate‐dependent rock strength across lithologies. Static strength and strain rate dominate, while geometric factors have minimal influence, enabling interpretable and uncertainty‐aware predictions for dynamic geomechanical applications. Abstract The dynamic
Hadi Fathipour‐Azar
wiley +1 more source
Bayesian Multivariate Two‐Stage Volatility‐Return Models (MTS‐VR)
Journal of Forecasting, EarlyView.ABSTRACT This paper develops a class of models to capture the joint dynamics of asset returns and multivariate volatility. Owing to its two‐stage construction, this class is referred to as the multivariate two‐stage volatility‐return (MTS‐VR) model.
Zhenglyu Huang +2 more
wiley +1 more source
State‐Dependent Market (In)Efficiency in Cryptocurrency Markets
Journal of Futures Markets, EarlyView.ABSTRACT This paper develops a meta‐learning framework to test whether cryptocurrency market inefficiency is static or state‐dependent. To resolve competing claims between the Efficient and Adaptive Market Hypotheses (AMH), we construct an adaptive trading strategy using high‐frequency data for 50 major cryptocurrencies.
Sasan Barak +2 more
wiley +1 more source