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1. Bayesian Network-Based Modeling and Operational Adjustment of Plantwide Flotation Industrial Process

Author:
Yan, Hao; Wang, Fuli; He, Dakuo; Zhao, Luping; Wang, Qingkai
Source:
Industrial & engineering chemistry process design and development 2020 v.59 no.5 pp. 2025-2035
ISSN:
1520-5045
Subject:
Bayesian theory,   etc   ; decision making; models;     Show all 3 Subjects
Abstract:
... The operational variables of the flotation industrial process (FIP) are controlled by the operators and are usually not adjusted in time. This makes it difficult to control the technical indexes such as concentrate grade within acceptable ranges. To resolve this problem, a Bayesian network (BN)-based modeling and operational adjustment method is investigated. Considering the complexity of modeling ...
DOI:
10.1021/acs.iecr.9b05803

2. Efficient probabilistic back analysis of spatially varying soil parameters for slope reliability assessment

Author:
Jiang, Shui-Hua; Huang, Jinsong; Qi, Xiao-Hui; Zhou, Chuang-Bing
Source:
Engineering geology 2020 v.271 pp. 105597
ISSN:
0013-7952
Subject:
Bayesian theory,   etc   ; multipliers; probability distribution; soil properties;     Show all 4 Subjects
Abstract:
... The probability distributions of soil parameters can be updated with limited site-specific information via probabilistic back analyses. The updated probability distributions can be further used for a more realistic slope reliability assessment. However, few attempts have been made to conduct probabilistic back analyses accounting for the inherent spatial variability of soil properties. The main ch ...
DOI:
10.1016/j.enggeo.2020.105597

3. Deep spatio-spectral Bayesian posterior for hyperspectral image non-i.i.d. noise removal

Author:
Zhang, Qiang; Yuan, Qiangqiang; Li, Jie; Sun, Fujun; Zhang, Liangpei
Source:
ISPRS journal of photogrammetry and remote sensing 2020 v.164 pp. 125-137
ISSN:
0924-2716
Subject:
Bayesian theory,   etc   ; hyperspectral imagery; models; noise pollution;     Show all 4 Subjects
Abstract:
... The noise pollution issue seriously obstructs subsequent interpretation and application of the hyperspectral image (HSI). In this work, differing from most existing HSI denoising methods ideally assumed that noise in different bands denotes independent & identically distributed (i.i.d.), we propose a novel HSI denoising approach focusing on non-i.i.d. noise removal. The presented framework collabo ...
DOI:
10.1016/j.isprsjprs.2020.04.010

4. A novel Bayesian approach for variable selection in linear regression models

Author:
Posch, Konstantin; Arbeiter, Maximilian; Pilz, Juergen
Source:
Computational statistics & data analysis 2020 v.144 pp. 106881
ISSN:
0167-9473
Subject:
Bayesian theory,   etc   ; algorithms; models; regression analysis;     Show all 4 Subjects
Abstract:
... A novel Bayesian approach to the problem of variable selection in multiple linear regression models is proposed. In particular, a hierarchical setting which allows for direct specification of a priori beliefs about the number of nonzero regression coefficients as well as a specification of beliefs that given coefficients are nonzero is presented. This is done by introducing a new prior for a rando ...
DOI:
10.1016/j.csda.2019.106881

5. Bayesian analysis of spatial generalized linear mixed models with Laplace moving average random fields

Author:
Walder, Adam; Hanks, Ephraim M.
Source:
Computational statistics & data analysis 2020 v.144 pp. 106861
ISSN:
0167-9473
Subject:
Bayesian theory,   etc   ; georeferencing; models; normal distribution; samplers;     Show all 5 Subjects
Abstract:
... Gaussian random field (GRF) models are widely used in spatial statistics to capture spatially correlated error. Gaussian processes can easily be replaced by the less commonly used Laplace moving averages (LMAs) in spatial generalized linear mixed models (SGLMMs). LMAs are shown to offer improved predictive power when the data exhibits localized spikes in the response. Further, SGLMMs with LMAs are ...
DOI:
10.1016/j.csda.2019.106861

6. Data-driven Bayesian network modelling to explore the relationships between SDG 6 and the 2030 Agenda

Author:
Requejo-Castro, David; Giné-Garriga, Ricard; Pérez-Foguet, Agustí
Source:
The Science of the total environment 2020 v.710 pp. 136014
ISSN:
0048-9697
Subject:
Bayesian theory,   etc   ; models; monitoring; sanitation; sustainable development;     Show all 5 Subjects
Abstract:
... The Sustainable Development Goals (SDGs) are presented as integrated and indivisible. Therefore, for monitoring purposes, conventional indicator-based frameworks need to be combined with approaches that capture and describe the links and interdependencies between the Goals and their targets. In this study, we propose a data-driven Bayesian network (BN) approach to identify and interpret SDGs inter ...
DOI:
10.1016/j.scitotenv.2019.136014

7. A novel power state evaluation method for the lithium battery packs based on the improved external measurable parameter coupling model

Author:
Wang, Shun-Li; Stroe, Daniel-Ioan; Fernandez, Carlos; Xiong, Li-Ying; Fan, Yong-Cun; Cao, Wen
Source:
Journal of cleaner production 2020 v.242 pp. 118506
ISSN:
0959-6526
Subject:
Bayesian theory,   etc   ; algorithms; lithium batteries; models; monitoring;     Show all 5 Subjects
Abstract:
... The power state evaluation plays a decisive influence on the safety implication of the lithium battery packs, and there is no effective online evaluation method so far due to the imbalance phenomenon among the internal connected battery cells, which cannot be abstained by the advancement of the materials and techniques. A novel power state mathematical evaluation method is proposed in this paper b ...
DOI:
10.1016/j.jclepro.2019.118506

8. Using approximate Bayesian computation for estimating parameters in the cue-based retrieval model of sentence processing

Author:
Vasishth, Shravan
Source:
MethodsX 2020 v.7 pp. 100850
ISSN:
2215-0161
Subject:
Bayesian theory,   etc   ; bioinformatics; models; reading; uncertainty;     Show all 5 Subjects
Abstract:
... A commonly used approach to parameter estimation in computational models is the so-called grid search procedure: the entire parameter space is searched in small steps to determine the parameter value that provides the best fit to the observed data. This approach has several disadvantages: first, it can be computationally very expensive; second, one optimal point value of the parameter is reported ...
DOI:
10.1016/j.mex.2020.100850
PubMed:
32300544
PubMed Central:
PMC7152701

9. Accounting for the uncertainty due to chemicals below the detection limit in mixture analysis

Author:
Hargarten, Paul M.; Wheeler, David C.
Source:
Environmental research 2020 v.186 pp. 109466
ISSN:
0013-9351
Subject:
Bayesian theory,   etc   ; detection limit; environmental exposure; regression analysis; uncertainty;     Show all 5 Subjects
Abstract:
... Simultaneous exposure to a mixture of chemicals over a lifetime may increase an individual's risk of disease to a greater extent than individual exposures. Researchers have used weighted quantile sum (WQS) regression to estimate the effect of multiple exposures in a manner that identifies the important (etiologically relevant) components in the mixture. However, complications arise when an experim ...
DOI:
10.1016/j.envres.2020.109466

10. Bayesian source reconstruction of an anomalous Selenium-75 release at a nuclear research institute

Author:
De Meutter, Pieter; Hoffman, Ian
Source:
Journal of environmental radioactivity 2020 v.218 pp. 106225
ISSN:
0265-931X
Subject:
Bayesian theory,   etc   ; models; radioactivity; radionuclides; research institutions; selenium; Belgium;     Show all 7 Subjects
Abstract:
... Atmospheric transport and dispersion models are important tools in radiation protection as they help to estimate the impact of radionuclides released into the atmosphere. In particular, such models can be used in combination with radionuclide observations to estimate unknown source term parameters, or to improve source term estimates obtained through other methods. In this paper, a Bayesian infere ...
DOI:
10.1016/j.jenvrad.2020.106225

11. Posterior distribution of nondifferentiable functions

Author:
Kitagawa, Toru; Montiel Olea, José Luis; Payne, Jonathan; Velez, Amilcar
Source:
Journal of econometrics 2020 v.217 no.1 pp. 161-175
ISSN:
0304-4076
Subject:
Bayesian theory,   etc   ; confidence interval; econometric models; economic analysis; economic theory;     Show all 5 Subjects
Abstract:
... This paper examines the asymptotic behavior of the posterior distribution of a possibly nondifferentiable function g(θ), where θ is a finite-dimensional parameter of either a parametric or semiparametric model. The main assumption is that the distribution of a suitable estimator θ̂n, its bootstrap approximation, and the Bayesian posterior for θ all agree asymptotically.It is shown that whenever g ...
DOI:
10.1016/j.jeconom.2019.10.009

12. Propagation thresholds of meteorological drought for triggering hydrological drought at various levels

Author:
Guo, Yi; Huang, Shengzhi; Huang, Qiang; Leng, Guoyong; Fang, Wei; Wang, Lu; Wang, Hao
Source:
The Science of the total environment 2020 v.712 pp. 136502
ISSN:
0048-9697
Subject:
Bayesian theory,   etc   ; basins; case studies; drought; groundwater; models; probability; watersheds;     Show all 8 Subjects
Abstract:
... What the extent of meteorological drought could trigger the corresponding hydrological drought with different levels? This question is an important topic in the field of drought propagation, which however has not been resolved. Therefore, a novel model based on a Bayesian network was proposed to address this issue in this study. In this model, the drought pooling and excluding methods were applied ...
DOI:
10.1016/j.scitotenv.2020.136502

13. Bayesian networks model for identification of the effective variables in the forecasting of debris flows occurrence

Author:
Banihabib, Mohammad Ebrahim; Tanhapour, Mitra; Roozbahani, Abbas
Source:
Environmental earth sciences 2020 v.79 no.8 pp. 179
ISSN:
1866-6280
Subject:
Bayesian theory,   etc   ; basins; mass movement; models; rain; uncertainty; watersheds; Iran;     Show all 8 Subjects
Abstract:
... Comprehensive assessment of debris flow hazards is a challenging issue due to the complexity and uncertainty of its factors. For this reason, the practical forecasting of debris flows requires developing a reliable and realistic forecasting model. In this paper, a Bayesian Networks (BNs) model is proposed for identification of debris flows events in the northern basins of Iran. BNs model illustrat ...
DOI:
10.1007/s12665-020-08911-w

14. Can sterilization help to prevent roaming in owned dogs and cats?

Author:
Baquero, Oswaldo Santos; da Silva Filho, Aluísio Pereira; Monsalve, Stefany; Gebara, Rosangela Ribeiro; Garcia, Rita de Cassia Maria; Sussai, Stefanie
Source:
Research in veterinary science 2020 v.129 pp. 180-184
ISSN:
0034-5288
Subject:
Bayesian theory,   etc   ; animal behavior; cats; dogs; male sterility; males; surveys;     Show all 7 Subjects
Abstract:
... In many locations, the highest proportion of roaming dogs and cats, might have owners. The prevention of roaming in owned dogs and cats is a key intervention to reduce the number of unsupervised animals in public spaces. Sterilization is an important population management intervention but it is unclear if, apart from its effects on birth rates and animal behavior, it also affects the roaming statu ...
DOI:
10.1016/j.rvsc.2020.01.021

15. Change points in annual peak streamflows: Method comparisons and historical change points in the United States

Author:
Ryberg, Karen R.; Hodgkins, Glenn A.; Dudley, Robert W.
Source:
Journal of hydrology 2020 v.583 pp. 124307
ISSN:
0022-1694
Subject:
Bayesian theory,   etc   ; anthropogenic activities; basins; landscapes; stream flow; United States;     Show all 6 Subjects
Abstract:
... Change-point, or step-trend, detection is an active area of research in statistics and an area of great interest in hydrology because change points may be evidence of natural or anthropogenic changes in climatic, hydrologic, or landscape processes. A common change-point technique is the Pettitt test; however, many change-point methods are now available and testing of methods has been limited. This ...
DOI:
10.1016/j.jhydrol.2019.124307

16. Statistical modeling of annual maximum precipitation in Oued El Gourzi Watershed, Algeria

Author:
Bella, Nassim; Dridi, Hadda; Kalla, Mahdi
Source:
Applied water science 2020 v.10 no.4 pp. 94
ISSN:
2190-5487
Subject:
Bayesian theory,   etc   ; probability distribution; statistical models; watersheds; Algeria;     Show all 5 Subjects
Abstract:
... This study aims to model annual maximum precipitation based on extreme value theory for the Oued El Gourzi Watershed, Algeria. A generalized extreme value (GEV) distribution was used to determine the probability distribution of extreme values and their dependency on time for the five stations distributed across the watershed. The non-stationary models are used to represent the GEV parameters assum ...
DOI:
10.1007/s13201-020-1175-6

17. A Bayesian mixture model for clustering circular data

Author:
Rodríguez, Carlos E.; Núñez-Antonio, Gabriel; Escarela, Gabriel
Source:
Computational statistics & data analysis 2020 v.143
ISSN:
0167-9473
Subject:
Bayesian theory,   etc   ; animals; data collection; models; normal distribution; probability;     Show all 6 Subjects
Abstract:
... Clustering complex circular phenomena is a common problem in different scientific disciplines. Examples include the clustering of directions of animal movement in the wild to identify migration patterns, and the classification of angular positions of meteorological events to investigate seasonality fluctuations. The main goal is to develop a novel methodology for clustering and classification of c ...
DOI:
10.1016/j.csda.2019.106842

18. Building generalized linear models with ultrahigh dimensional features: A sequentially conditional approach

Author:
Zheng, Qi; Hong, Hyokyoung G.; Li, Yi
Source:
Biometrics 2020 v.76 no.1 pp. 47-60
ISSN:
0006-341X
Subject:
Bayesian theory,   etc   ; biometry; genomics; linear models; myeloma; prediction; screening;     Show all 7 Subjects
Abstract:
... Conditional screening approaches have emerged as a powerful alternative to the commonly used marginal screening, as they can identify marginally weak but conditionally important variables. However, most existing conditional screening methods need to fix the initial conditioning set, which may determine the ultimately selected variables. If the conditioning set is not properly chosen, the methods m ...
DOI:
10.1111/biom.13122
PubMed:
31350909
PubMed Central:
PMC7136011

19. Quantification of prior impact in terms of effective current sample size

Author:
Wiesenfarth, Manuel; Calderazzo, Silvia
Source:
Biometrics 2020 v.76 no.1 pp. 326-336
ISSN:
0006-341X
Subject:
Bayesian theory,   etc   ; biometry; clinical trials; computer software; models; patients;     Show all 6 Subjects
Abstract:
... Bayesian methods allow borrowing of historical information through prior distributions. The concept of prior effective sample size (prior ESS) facilitates quantification and communication of such prior information by equating it to a sample size. Prior information can arise from historical observations; thus, the traditional approach identifies the ESS with such a historical sample size. However, ...
DOI:
10.1111/biom.13124

20. Semiparametric mixed‐scale models using shared Bayesian forests

Author:
Linero, Antonio R.; Sinha, Debajyoti; Lipsitz, Stuart R.
Source:
Biometrics 2020 v.76 no.1 pp. 131-144
ISSN:
0006-341X
Subject:
Bayesian theory,   etc   ; biometry; heteroskedasticity; information exchange; models; regression analysis; surveys;     Show all 7 Subjects
Abstract:
... This paper demonstrates the advantages of sharing information about unknown features of covariates across multiple model components in various nonparametric regression problems including multivariate, heteroscedastic, and semicontinuous responses. In this paper, we present a methodology which allows for information to be shared nonparametrically across various model components using Bayesian sum‐o ...
DOI:
10.1111/biom.13107