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Bayesian Model Selection in the Analysis of Cointegration [PDF]

In this paper we present the Bayesian model selection procedure within the class of cointegrated processes. In order to make inference about the cointegration space we use the class of Matrix Angular Central Gaussian distributions. To carry out posterior
Justyna Wróblewska
core

Penalized flexible Bayesian quantile regression [PDF]

, 2012
Copyright © 2012 SciResThis article has been made available through the Brunel Open Access Publishing Fund.The selection of predictors plays a crucial role in building a multiple regression model. Indeed, the choice of a suitable subset of predictors can
Yu, K, Alkenani, A, Alhamzawi, R
core +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source

Location optimization of cold chain logistics parks based on Bayesian probability theory and K-means clustering analysis in China

Scientific Reports
The site selection of cold-chain logistics parks is an indispensable part of their planning and construction. This study aims to establish site selection model provide a scientific and sustainable for selecting and determining optimal cold chain ...
Lu Wang, Xuannuo Liu, Xuhui Wang, Yijun Jiang +3 more
doaj +1 more source

Bayesian Portfolio Selection with Gaussian Mixture Returns [PDF]

Markowitz portfolio selection is challenged by huge implementation barriers. This paper addresses the parameter uncertainty and deviation from normality in a Bayesian framework. The non-normal asset returns are modeled as finite Gaussian mixtures.
Qian, Hang
core

Bayesian Regularisation in Structured Additive Regression Models for Survival Data [PDF]

, 2008
During recent years, penalized likelihood approaches have attracted a lot of interest both in the area of semiparametric regression and for the regularization of high-dimensional regression models.
Konrath, Susanne, Kneib, Thomas, Fahrmeir, Ludwig +2 more
core +1 more source

Artificial Intelligence Meets Micro/Nanorobotics

Advanced Materials, EarlyView.
Artificial intelligence is transforming micro‐ and nanorobots from externally controlled, task‐specific machines into adaptive, autonomous systems. Machine learning, multimodal perception, digital twins, AI‐guided materials and geometry design enhance propulsion, localization, decision‐making, whichaccelerates clinical and environmental applications ...
Fatma M. Yurtsever +6 more
wiley +1 more source

Bayesian estimation in NONMEM

CPT: Pharmacometrics & Systems Pharmacology
Bayesian estimation is a powerful but underutilized tool for answering drug development questions. In this tutorial, the principles of Bayesian model development, assessment, and prior selection will be outlined.
Curtis K. Johnston +5 more
doaj +1 more source

Bayesian Penalized Method for Streaming Feature Selection

IEEE Access, 2019
The online feature selection with streaming features has become more and more important in recent years. In contrast to standard feature selection method, streaming feature selection method can select feature dynamically without exploring full feature ...
Xiao-Ting Wang, Xin-Ze Luan
doaj +1 more source

Model Selection Criteria for Segmented Time Series from a Bayesian Approach to Information Compression [PDF]

The principle that the simplest model capable of describing observed phenomena should also correspond to the best description has long been a guiding rule of inference. In this paper a Bayesian approach to formally implementing this principle is employed
Brian Hanlon, Catherine Forbes
core

medicine
model selection