Results 21 to 30 of about 15,911,760 (295)

Spatially-dependent Bayesian model selection for disease mapping. [PDF]

Stat Methods Med Res, 2018
Carroll R, Lawson AB, Faes C, Kirby RS, Aregay M, Watjou K.   +5 more
europepmc   +2 more sources

Feature Selection Based on Sparse Bayesian Model [PDF]

Jisuanji gongcheng, 2017
Through using sparse Bayesian inference thought,a Feature Selection Probabilistic Classification Vector Machine (FPCVM) is designed which can learn optimal classifier and automatically select the most relevant feature subset.FPCVM is an extension of ...
ZHU Pu,HUANG Zhangjin
doaj   +1 more source

Radiometric Scale Transfer Using Bayesian Model Selection

Proceedings, 2020
The key input quantity to climate modelling and weather forecasts is the solar beam irradiance, i.e., the primary amount of energy provided by the sun.
Donald W. Nelson, Udo von Toussaint
doaj   +1 more source

Bayesian parameter inference and model selection by population annealing in systems biology. [PDF]

PLoS ONE, 2014
Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection.
Yohei Murakami
doaj   +1 more source

Information‐Theoretic Scores for Bayesian Model Selection and Similarity Analysis: Concept and Application to a Groundwater Problem

Water Resources Research, 2023
Bayesian model selection (BMS) and Bayesian model justifiability analysis (BMJ) provide a statistically rigorous framework for comparing competing models through the use of Bayesian model evidence (BME).
Maria Fernanda Morales Oreamuno, Sergey Oladyshkin, Wolfgang Nowak   +2 more
doaj   +1 more source

Robust and parallel Bayesian model selection [PDF]

Computational Statistics & Data Analysis, 2018
Effective and accurate model selection is an important problem in modern data analysis. One of the major challenges is the computational burden required to handle large data sets that cannot be stored or processed on one machine. Another challenge one may encounter is the presence of outliers and contaminations that damage the inference quality.
Michael Minyi Zhang, Henry Lam, Lizhen Lin   +2 more
openaire   +3 more sources

Bayesian Reordering Model with Feature Selection [PDF]

Proceedings of the Ninth Workshop on Statistical Machine Translation, 2014
In phrase-based statistical machine translation systems, variation in grammatical structures between source and target languages can cause large movements of phrases. Modeling such movements is crucial in achieving translations of long sentences that appear natural in the target language.
Alrajeh, Abdullah, Niranjan, Mahesan
openaire   +2 more sources

Bayesian model averaging: improved variable selection for matched case-control studies

Epidemiology, Biostatistics and Public Health, 2019
Background: The problem of variable selection for risk factor modeling is an ongoing challenge in statistical practice. Classical methods that select one subset of exploratory risk factors dominate the medical research field.
Yi Mu, Isaac See MD, Jonathan R. Edwards MSc   +2 more
doaj   +1 more source

Bayesian model selection for group studies [PDF]

NeuroImage, 2009
Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of competing hypotheses about the mechanisms that generated observed data. BMS has recently found widespread application in neuroimaging, particularly in the context of dynamic causal modelling (DCM).
Klaas Enno Stephan, Will D. Penny, Jean Daunizeau, Rosalyn J. Moran, Karl J. Friston   +4 more
openaire   +7 more sources

BICOSS: Bayesian iterative conditional stochastic search for GWAS

BMC Bioinformatics, 2022
Background Single marker analysis (SMA) with linear mixed models for genome wide association studies has uncovered the contribution of genetic variants to many observed phenotypes. However, SMA has weak false discovery control.
Jacob Williams, Marco A. R. Ferreira, Tieming Ji   +2 more
doaj   +1 more source