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1. Quantifying input error in hydrologic modeling using the Bayesian error analysis with reordering (BEAR) approach

Author:
Wu, Xia; Marshall, Lucy; Sharma, Ashish
Source:
Journal of hydrology 2021 v.598 pp. 126202
ISSN:
0022-1694
Subject:
Bayesian theory,   etc   ; algorithms; calibration; memory;     Show all 4 Subjects
Abstract:
... Input error in hydrologic models, mainly arising from observed precipitation, can impair model calibration. Precise and reliable identification of input error is important for improved model parameter estimation. However, limited information about the nature of the error and the memory of the hydrologic system make it challenging to disentangle input error from the total residual error. Based on a ...
DOI:
10.1016/j.jhydrol.2021.126202

2. The impact of genetic diversity statistics on model selection between coalescents

Author:
Freund, Fabian; Siri-Jégousse, Arno
Source:
Computational statistics & data analysis 2021 v.156 pp. 107055
ISSN:
0167-9473
Subject:
Bayesian theory,   etc   ; genetic variation; models;     Show all 3 Subjects
Abstract:
... Modeling genetic diversity needs an underlying genealogy model. To choose a fitting model based on genetic data, one can perform model selection between classes of genealogical trees, e.g. Kingman’s coalescent with exponential growth or multiple merger coalescents. Such selection can be based on many different statistics measuring genetic diversity. A random forest based Approximate Bayesian Compu ...
DOI:
10.1016/j.csda.2020.107055

3. Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry

Author:
Gundry, Luke; Guo, Si-Xuan; Kennedy, Gareth; Keith, Jonathan; Robinson, Martin; Gavaghan, David; Bond, Alan M.; Zhang, Jie
Source:
Chemical communications 2021 v.57 no.15 pp. 1855-1870
ISSN:
1364-548X
Subject:
Bayesian theory,   etc   ; automation; electrochemistry; voltammetry;     Show all 4 Subjects
Abstract:
... Advanced data analysis tools such as mathematical optimisation, Bayesian inference and machine learning have the capability to revolutionise the field of quantitative voltammetry. Nowadays such approaches can be implemented routinely with widely available, user-friendly modern computing languages, algorithms and high speed computing to provide accurate and robust methods for quantitative compariso ...
DOI:
10.1039/d0cc07549c

4. Nonparametric Bayes subject to overidentified moment conditions

Author:
Gallant, A. Ronald
Source:
Journal of econometrics 2021
ISSN:
0304-4076
Subject:
Bayesian theory,   etc   ; Markov chain; algorithms;     Show all 3 Subjects
Abstract:
... Nonparametric Bayesian estimation subject to overidentified moment equations is a challenge because the support of the posterior is a manifold of lower dimension than the number of model parameters. The manifold therefore has Lebesgue measure zero thus inhibiting the use of the most commonly used Bayesian estimation method: MCMC (Markov Chain Monte Carlo). This study proposes an effective MCMC alg ...
DOI:
10.1016/j.jeconom.2021.02.005

5. Age-at-death Estimation in Adults and Verification of a Forensic International Methodology using Single-Rooted teeth: An Approach for a Peruvian Context

Author:
Parra, Roberto C.; Suárez-Ponce, Daniel G.; Escalante-Flórez, Karen J.; Condori, Lucio A.; Calcina-Mendoza, Oswaldo; Peralta-Cerro, Liz M.; Rosas-Moyano, Glicerio A.
Source:
Forensic science international 2021 v.3 pp. 100176
ISSN:
2665-9107
Subject:
Bayesian theory,   etc   ; calibration; databases; forensic sciences; Peru;     Show all 5 Subjects
Abstract:
... From a forensic perspective, age-at-death estimation in adults is a constant concern that impacts the development of investigations regarding unidentified dead bodies and the need to identify them. Several procedures for age-at-death estimation in adults have been introduced in Peru, and are used in various fields such as bioarcheology and forensics, but very little is known about the precision an ...
DOI:
10.1016/j.fsir.2021.100176

6. Clusterwise functional linear regression models

Author:
Li, Ting; Song, Xinyuan; Zhang, Yingying; Zhu, Hongtu; Zhu, Zhongyi
Source:
Computational statistics & data analysis 2021 v.158 pp. 107192
ISSN:
0167-9473
Subject:
Bayesian theory,   etc   ; algorithms; data analysis; regression analysis;     Show all 4 Subjects
Abstract:
... Classical clusterwise linear regression is a useful method for investigating the relationship between scalar predictors and scalar responses with heterogeneous variation of regression patterns for different subgroups of subjects. This paper extends the classical clusterwise linear regression to incorporate multiple functional predictors by representing the functional coefficients in terms of a fun ...
DOI:
10.1016/j.csda.2021.107192

7. Population projections from holey matrices: Using prior information to estimate rare transition events

Author:
Tremblay, Raymond L.; Tyre, Andrew J.; Pérez, Maria-Eglée; Ackerman, James D.
Source:
Ecological modelling 2021 v.447 pp. 109526
ISSN:
0304-3800
Subject:
Bayesian theory,   etc   ; endangered species; models; reproduction;     Show all 4 Subjects
Abstract:
... Population projection matrices are a common means for predicting short- and long-term population persistence for rare, threatened and endangered species. Data from such species can suffer from small sample sizes and consequently miss rare demographic events resulting in incomplete or biologically unrealistic life cycle trajectories. Matrices with missing values (zeros; e.g., no observation of seed ...
DOI:
10.1016/j.ecolmodel.2021.109526

8. Post-processing of reservoir releases to support real-time reservoir operation and its effects on downstream hydrological alterations

Author:
He, Shan; Yin, Xin'an; Shao, Yawen; Yang, Wei
Source:
Journal of hydrology 2021 v.596 pp. 126073
ISSN:
0022-1694
Subject:
Bayesian theory,   etc   ; hydrology; models; rivers; uncertainty;     Show all 5 Subjects
Abstract:
... Reservoir operation relies on two sets of flow data to conduct error analysis: inflow data and release data. Previous studies applied only the inflow data to reduce errors and improve the reservoir’s operational effectiveness. Little attention has been paid to the accuracy of the release data. However, release data provides more accurate information than inflow data for the protection of downstrea ...
DOI:
10.1016/j.jhydrol.2021.126073

9. Using integrated Bayesian-Petri Net method for individual impact assessment of domino effect accidents

Author:
Dueñas Santana, Julio Ariel; Orozco, Jesús Luis; Febles Lantigua, Dainelys; Furka, Daniel; Furka, Samuel; García Cruz, Ariel
Source:
Journal of cleaner production 2021 v.294 pp. 126236
ISSN:
0959-6526
Subject:
Bayesian theory,   etc   ; human population; models; risk;     Show all 4 Subjects
Abstract:
... One of the aims of Cleaner Production refers to the reduction of risks for the human population. Domino effect accidents trigger the worst consequences in process industries. To date, several techniques and models have been used for predicting domino effect probability, such as Bayesian networks and Petri Nets. In addition, these methodologies can be combined with other classical ones, for instanc ...
DOI:
10.1016/j.jclepro.2021.126236

10. Bayesian Bivariate Semiparametric Spatial Models for Ozone and PM2.5 Emissions

Author:
Cheng, Wen; Gill, Gurdiljot Singh; Wen, Frank; Zhou, Jiao
Source:
Environmental modeling and assessment 2021 v.26 no.2 pp. 237-249
ISSN:
1420-2026
Subject:
Bayesian theory,   etc   ; data collection; ozone; poverty; traffic;     Show all 5 Subjects
Abstract:
... This study focused on the development of Bayesian bivariate semiparametric models for ozone and PM2.5 emissions. The semiparametric models rely on a Dirichlet mixture, which accounts for unobserved heterogeneity by employing random distribution for the intercept of each entity. The models with and without spatially structured random effects were also evaluated. Overall, four models were developed: ...
DOI:
10.1007/s10666-020-09732-8

11. Total-evidence analysis resolves the phylogenetic position of an enigmatic group of Paederinae rove beetles (Coleoptera: Staphylinidae)

Author:
Żyła, Dagmara; Bogri, Amalia; Heath, Tracy A.; Solodovnikov, Alexey
Source:
Molecular phylogenetics and evolution 2021 v.157 pp. 107059
ISSN:
1055-7903
Subject:
Bayesian theory,   etc   ; Neotropics; Staphylinidae; mitochondria; phylogeny;     Show all 5 Subjects
Abstract:
... Paederinae is one of the most diverse subfamilies among rove beetles, yet their evolutionary history remains poorly understood. This is attributed to the limited number of phylogenetic studies, which either sought answers at a shallower taxonomic level or included limited taxon sampling. Especially problematic is the position of the rare Neotropical tribe Cylindroxystini, morphologically one of th ...
DOI:
10.1016/j.ympev.2020.107059

12. Triqler for MaxQuant: Enhancing Results from MaxQuant by Bayesian Error Propagation and Integration

Author:
The, Matthew; Käll, Lukas
Source:
Journal of proteome research 2021 v.20 no.4 pp. 2062-2068
ISSN:
1535-3907
Subject:
Bayesian theory,   etc   ; proteome; proteomics; research; uncertainty;     Show all 5 Subjects
Abstract:
... Error estimation for differential protein quantification by label-free shotgun proteomics is challenging due to the multitude of error sources, each contributing uncertainty to the final results. We have previously designed a Bayesian model, Triqler, to combine such error terms into one combined quantification error. Here we present an interface for Triqler that takes MaxQuant results as input, al ...
DOI:
10.1021/acs.jproteome.0c00902

13. A Generic Method for Estimating and Smoothing Multispecies Biodiversity Indicators Using Intermittent Data

Author:
Freeman, Stephen N.; Isaac, Nicholas J. B.; Besbeas, Panagiotis; Dennis, Emily B.; Morgan, Byron J. T.
Source:
Journal of agricultural, biological, and environmental statistics 2021 v.26 no.1 pp. 71-89
ISSN:
1085-7117
Subject:
Bayesian theory,   etc   ; biodiversity; issues and policy; probability;     Show all 4 Subjects
Abstract:
... Biodiversity indicators summarise extensive, complex ecological data sets and are important in influencing government policy. Component data consist of time-varying indices for each of a number of different species. However, current biodiversity indicators suffer from multiple statistical shortcomings. We describe a state-space formulation for new multispecies biodiversity indicators, based on rat ...
DOI:
10.1007/s13253-020-00410-6

14. How weather affects the decomposition of total factor productivity in U.S. agriculture

Author:
Plastina, Alejandro; Lence, Sergio H.; Ortiz‐Bobea, Ariel
Source:
Agricultural economics 2021 v.52 no.2 pp. 215-234
ISSN:
0169-5150
Subject:
Bayesian theory,   etc   ; agricultural economics; meteorological data; models;     Show all 4 Subjects
Abstract:
... This study illustrates and quantifies how overlooking the impact of weather shocks can affect the measurement and decomposition of agricultural total factor productivity (TFP) change. The underlying technology is represented by a flexible input distance function with quasi‐fixed inputs estimated with Bayesian methods. Using agricultural production and weather data for 16 states in the Pacific Regi ...
DOI:
10.1111/agec.12615

15. Study design synopsis: Battle in the stable: Bayesianism versus Frequentism

Author:
Detilleux, Johann
Source:
Equine veterinary journal 2021 v.53 no.2 pp. 199-204
ISSN:
0425-1644
Subject:
Bayesian theory,   etc   ; experimental design; horses; veterinary medicine;     Show all 4 Subjects
Abstract:
... Frequentism dominates scientific practice although Bayesianism may provide an alternative, especially when analysing data from complex, high‐dimensional models. The key differences between Bayesianism and Frequentism are highlighted in the introduction. Next, I review the different stages of Bayesian statistical reasoning in a research setting, explain the key concepts and illustrate them with toy ...
DOI:
10.1111/evj.13351

16. Bayesian regional flood frequency analysis with GEV hierarchical models under spatial dependency structures

Author:
Sampaio, Júlio; Costa, Veber
Source:
Hydrological sciences journal 2021 v.66 no.3 pp. 422-433
ISSN:
2150-3435
Subject:
Bayesian theory,   etc   ; models; prediction; rivers; watersheds; Brazil;     Show all 6 Subjects
Abstract:
... Bayesian hierarchical models have been increasingly used in regional flood frequency analysis due to their flexibility and ability to accommodate the spatial variability of flooding processes in distribution parameters. Hierarchical models based on the generalized extreme value (GEV) distribution are useful since they may combine scaling properties and distinct degrees of pooling in the shape para ...
DOI:
10.1080/02626667.2021.1873997

17. Using visual encounter data to improve capture–recapture abundance estimates

Author:
Joseph, Maxwell B.; Knapp, Roland A.
Source:
Ecosphere 2021 v.12 no.2 pp. e03370
ISSN:
2150-8925
Subject:
Bayesian theory,   etc   ; mark-recapture studies; population size;     Show all 3 Subjects
Abstract:
... Capture–recapture studies are widely used in ecology to estimate population sizes and demographic rates. In some capture–recapture studies, individuals may be visually encountered but not identified. For example, if individual identification is only possible upon capture and individuals escape capture, visual encounters can result in failed captures where individual identities are unknown. In such ...
DOI:
10.1002/ecs2.3370

18. Stochastic Gradient Markov Chain Monte Carlo

Author:
Nemeth, Christopher; Fearnhead, Paul
Source:
Journal of the American Statistical Association 2021 v.116 no.533 pp. 433-450
ISSN:
1537-274X
Subject:
Bayesian theory,   etc   ; Markov chain; algorithms; data collection;     Show all 4 Subjects
Abstract:
... Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the gold standard technique for Bayesian inference. They are theoretically well-understood and conceptually simple to apply in practice. The drawback of MCMC is that performing exact inference generally requires all of the data to be processed at each iteration of the algorithm. For large datasets, the computational cost of MCMC ...
DOI:
10.1080/01621459.2020.1847120

19. PA‐CRM: A continuous reassessment method for pediatric phase I oncology trials with concurrent adult trials

Author:
Li, Yimei; Yuan, Ying
Source:
Biometrics 2020 v.76 no.4 pp. 1364-1373
ISSN:
0006-341X
Subject:
Bayesian theory,   etc   ; adults; patients;     Show all 3 Subjects
Abstract:
... Pediatric phase I trials are usually carried out after the adult trial testing the same agent has started, but not completed yet. As the pediatric trial progresses, in light of the accrued interim data from the concurrent adult trial, the pediatric protocol often is amended to modify the original pediatric dose escalation design. In practice, this is done frequently in an ad hoc way, interrupting ...
DOI:
10.1111/biom.13217

20. Hybridizing physical and data-driven prediction methods for physicochemical properties

Author:
Jirasek, Fabian; Bamler, Robert; Mandt, Stephan
Source:
Chemical communications 2020 v.56 no.82 pp. 12407-12410
ISSN:
1364-548X
Subject:
Bayesian theory,   etc   ; models; prediction;     Show all 3 Subjects
Abstract:
... We present a generic way to hybridize physical and data-driven methods for predicting physicochemical properties. The approach ‘distills’ the physical method's predictions into a prior model and combines it with sparse experimental data using Bayesian inference. We apply the new approach to predict activity coefficients at infinite dilution and obtain significant improvements compared to the physi ...
DOI:
10.1039/d0cc05258b