A special issue on Bayesian inference: challenges, perspectives and prospects

Robert, Christian P.; Rousseau, Judith

doi:10.1098/rsta.2022.0155

Cited by 3 publications

(3 citation statements)

References 26 publications

(28 reference statements)

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“…The Bayesian modeling framework assumes that the parameter estimates are stochastic and hence assumes a distribution for each parameter estimate, including hyperparameters. [36][37][38] The main criticism of such an assumption is that it depends on subjective prior assumptions, which might result in extreme or confusing conclusions. However, these priors also represent an advantage since they provide a tool for statisticians to be transparent about their assumptions.…”

Section: Bayesian Modeling Frameworkmentioning

confidence: 99%

“…However, these priors also represent an advantage since they provide a tool for statisticians to be transparent about their assumptions. [36][37][38] Overweighting or informative priors have the potential to mislead statistical inferences about the data being studied since parameter estimates would be much influenced by such priors rather than the data under study. In Bayesian modeling, conjugate or non-informative priors are recommended in order to allow the data to have more influence on the parameter estimates.…”

Section: Bayesian Modeling Frameworkmentioning

confidence: 99%

“…In Bayesian modeling, conjugate or non-informative priors are recommended in order to allow the data to have more influence on the parameter estimates. [36][37][38] Using traceplots or density plots of parameter estimates, 39 Markov Chain Monte Carlo (MCMC) methods determine when a model's parameters have converged, greatly facilitating Bayesian inference for complex models. Nonetheless, these methods are computationally intensive and encounter various convergence issues.…”

Section: Bayesian Modeling Frameworkmentioning

confidence: 99%

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Spatio-temporal characteristics of Tuberculosis in Ghana

Iddrisu,

A. Amikiya,

Kwame Bukari

2024

F1000Res

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Background Tuberculosis (TB) continues to be a prominent contributor to global mortality, standing as the second most fatal infectious disease and holding the seventh position among the top ten causes of death in Ghana. There is insufficient literature regarding the utilization of Bayesian hierarchical models, specifically within the framework of Integrated Nested Laplace Approximation (INLA), for examining the spatial and spatio-temporal dynamics of tuberculosis risk in Ghana. This study addresses this gap by determining TB hotspots regions in Ghana using the Bayesian modeling framework within the INLA. Methods TB data were sourced from the Ghana Health Service and National Tuberculosis Programme for the 10 administrative regions of Ghana, from 2008 to 2017. The relative risk of TB for each region and year was estimated utilizing Bayesian spatial and spatio-temporal modeling frameworks. Baseline predictors of TB risk were also considered. Maps for TB risks were created to visualized regions with TB hotspots. Model fitting and parameter estimation were conducted using R version 4.3.2. Results Among the baseline predictors, factors such as TB cure rate, TB success rate, knowledge about TB, HIV prevalence, percentage of literacy, and high income were found to be most significant in influencing the TB risk across the ten regions in Ghana. We noted an increased risk of TB infection in the Northern zone and the Eastern and Greater Accra regions in the Southern zone. Spatio-temporal distribution of TB infection risk was predominantly concentrated in the Southern zone. Clustering of TB risk was observed among neighboring regions. Conclusion To achieve a significant reduction in TB cases, it is essential to allocate resources to TB hotspots regions and also implement measures to control significant predictors of TB infection risk.

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Section: Bayesian Modeling Frameworkmentioning

confidence: 99%

Section: Bayesian Modeling Frameworkmentioning

confidence: 99%

Section: Bayesian Modeling Frameworkmentioning

confidence: 99%

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Spatio-temporal characteristics of Tuberculosis in Ghana

Iddrisu,

A. Amikiya,

Kwame Bukari

2024

F1000Res

View full text Add to dashboard Cite

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Spatio-temporal characteristics of Tuberculosis in Ghana

Iddrisu,

A. Amikiya,

Kwame Bukari

2024

F1000Res

View full text Add to dashboard Cite

Background Tuberculosis (TB) remains a key contributor to global mortality; ranks second as the most fatal infectious disease and seventh among the top ten causes of death in Ghana. There is insufficient literature regarding the utilization of Bayesian hierarchical models for examining the spatial and spatio-temporal dynamics of tuberculosis risk in Ghana. Aim This study addresses this gap by determining TB hotspots regions in Ghana using the Bayesian modeling framework. Methods TB data were obtained from the Ghana Health Service and National Tuberculosis Programme for the 10 administrative regions of Ghana, from 2008 to 2017. Spatial and spatio-temporal TB relative risk for each region were estimated under the Bayesian modeling frameworks. Maps for TB risks were created to visualized regions with TB hotspots. Model fitting and parameter estimation were conducted using integrated nested Laplace approximation via R version 4.3.2. Results Among the baseline predictors, TB cure rate, TB success rate, knowledge about TB, human immunodeficiency virus (HIV) prevalence, percentage of literacy, and high income were found to be most significant predictors of TB risk in Ghana. We noted an increased risk of TB infection in the Northern zone and the Eastern and Greater Accra regions in the Southern zone. Spatio-temporal distribution of TB infection risk was predominantly concentrated in the Southern zone. Clustering of TB risk was observed among neighboring regions. Conclusion Factors influencing tuberculosis (TB) risk in Ghana are TB cure rate, TB success rate, knowledge about TB, HIV prevalence, literacy rate, and income level. The risk distribution was mainly concentrated in the Southern zone, with clusters of TB risk observed among neighboring regions. To achieve a significant reduction in TB cases, it is essential to allocate resources to TB hotspots regions and also implement measures to control significant predictors of TB infection risk.

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Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects

Wang,

2024

Agronomy

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The frequent occurrence of global climate change and natural disasters highlights the importance of precision agricultural monitoring, yield forecasting, and early warning systems. The data assimilation method provides a new possibility to solve the problems of low accuracy of yield prediction, strong dependence on the field, and poor adaptability of the model in traditional agricultural applications. Therefore, this study makes a systematic literature retrieval based on Web of Science, Scopus, Google Scholar, and PubMed databases, introduces in detail the assimilation strategies based on many new remote sensing data sources, such as satellite constellation, UAV, ground observation stations, and mobile platforms, and compares and analyzes the progress of assimilation models such as compulsion method, model parameter method, state update method, and Bayesian paradigm method. The results show that: (1) the new remote sensing platform data assimilation shows significant advantages in precision agriculture, especially in emerging satellite constellation remote sensing and UAV data assimilation. (2) SWAP model is the most widely used in simulating crop growth, while Aquacrop, WOFOST, and APSIM models have great potential for application. (3) Sequential assimilation strategy is the most widely used algorithm in the field of agricultural data assimilation, especially the ensemble Kalman filter algorithm, and hierarchical Bayesian assimilation strategy is considered to be a promising method. (4) Leaf area index (LAI) is considered to be the most preferred assimilation variable, and the study of soil moisture (SM) and vegetation index (VIs) has also been strengthened. In addition, the quality, resolution, and applicability of assimilation data sources are the key bottlenecks that affect the application of data assimilation in the development of precision agriculture. In the future, the development of data assimilation models tends to be more refined, diversified, and integrated. To sum up, this study can provide a comprehensive reference for agricultural monitoring, yield prediction, and crop early warning by using the data assimilation model.

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A special issue on Bayesian inference: challenges, perspectives and prospects

Cited by 3 publications

References 26 publications

Spatio-temporal characteristics of Tuberculosis in Ghana

Spatio-temporal characteristics of Tuberculosis in Ghana

Spatio-temporal characteristics of Tuberculosis in Ghana

Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects

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