Bayesian Inference Research Articles

Residual network improves the prediction accuracy of genomic selection.

Genetic improvement of complex traits in animal and plant breeding depends on the efficient and accurate estimation of breeding values. Deep learning methods have been shown to be not superior over traditional genomic selection (GS) methods, partially due to the degradation problem (i.e. with the increase of the model depth, the performance of the deeper model deteriorates). Since the deep learning method residual network (ResNet) is designed to solve gradient degradation, we examined its performance and factors related to its prediction accuracy in GS. Here we compared the prediction accuracy of conventional genomic best linear unbiased prediction, Bayesian methods (BayesA, BayesB, BayesC, and Bayesian Lasso), and two deep learning methods, convolutional neural network and ResNet, on three datasets (wheat, simulated and real pig data). ResNet outperformed other methods in both Pearson's correlation coefficient (PCC) and mean squared error (MSE) on the wheat and simulated data. For the pig backfat depth trait, ResNet still had the lowest MSE, whereas Bayesian Lasso had the highest PCC. We further clustered the pig data into four groups and, on one separated group, ResNet had the highest prediction accuracy (both PCC and MSE). Transfer learning was adopted and capable of enhancing the performance of both convolutional neural network and ResNet. Taken together, our findings indicate that ResNet could improve GS prediction accuracy, affected potentially by factors such as the genetic architecture of complex traits, data volume, and heterogeneity.

Antimicrobial resistance and population genomics of emerging multidrug-resistant Salmonella 4,[5],12:i:- in Guangdong, China.

Salmonella 4,[5],12:i:-, a monophasic variant of Salmonella Typhimurium, has emerged as a global cause of multidrug-resistant salmonellosis and has become endemic in many developing and developed countries, especially in China. Here, we have sequenced 352 clinical isolates in Guangdong, China, during 2009-2019 and performed a large-scale collection of Salmonella 4,[5],12:i:- with whole genome sequencing (WGS) data across the globe, to better understand the population structure, antimicrobial resistance (AMR) genomic characterization, and transmission routes of Salmonella 4,[5],12:i:- across Guangdong. Salmonella 4,[5],12:i:- strains showed broad genetic diversity; Guangdong isolates were found to be widely distributed among the global lineages. Of note, we identified the formation of a novel Guangdong clade (Bayesian analysis of population structure lineage 1 [BAPS1]) genetically diversified from the global isolates and likely emerged around 1990s. BAPS1 exhibits unique genomic features, including large pan-genome, decreased ciprofloxacin susceptibility due to mutation in gyrA and carriage of plasmid-mediated quinolone resistance (PMQR) genes, and the multidrug-resistant IncHI2 plasmid. Furthermore, high genetic similarity was found between strains collected from Guangdong, Europe, and North America, indicating the association with multiple introductions from overseas. These results suggested that global dissemination and local clonal expansion simultaneously occurred in Guangdong, China, and horizontally acquired resistance to first-line and last-line antimicrobials at local level, underlying emergences of extensive drug and pan-drug resistance. Our findings have increased the knowledge of global and local epidemics of Salmonella 4,[5],12:i:- in Guangdong, China, and provided a comprehensive baseline data set essential for future molecular surveillance.IMPORTANCESalmonella 4,[5],12:i:- has been regarded as the predominant pandemic serotype causing diarrheal diseases globally, while multidrug resistance (MDR) constitutes great public health concerns. This study provided a detailed and comprehensive genome-scale analysis of this important Salmonella serovar in the past decade in Guangdong, China. Our results revealed the complexity of two distinct transmission modes, namely global transmission and local expansion, circulating in Guangdong over a decade. Using phylogeography models, the origin of Salmonella 4,[5],12:i:- was predicted from two aspects, year and country, that is, Salmonella 4,[5],12:i:- emerged in 1983, and was introduced from the UK, and subsequently differentiated into the local endemic lineage circa 1991. Additionally, based on the pan-genome analysis, it was found that the gene accumulation rate in local endemic BAPS 1 lineage was higher than in other lineages, and the horizontal transmission of MDR IncHI2 plasmid associated with high resistance played a major role, which showed the potential threat to public health.

Heterogeneity, Bayesian thinking, and phenotyping in critical care: A primer.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. To familiarize clinicians with the emerging concepts in critical care research of Bayesian thinking and personalized medicine through phenotyping and explain their clinical relevance by highlighting how they address the issues of frequent negative trials and heterogeneity of treatment effect. The past decades have seen many negative (effect-neutral) critical care trials of promising interventions, culminating in calls to improve the field's research through adopting Bayesian thinking and increasing personalization of critical care medicine through phenotyping. Bayesian analyses add interpretive power for clinicians as they summarize treatment effects based on probabilities of benefit or harm, contrasting with conventional frequentist statistics that either affirm or reject a null hypothesis. Critical care trials are beginning to include prospective Bayesian analyses, and many trials have undergone reanalysis with Bayesian methods. Phenotyping seeks to identify treatable traits to target interventions to patients expected to derive benefit. Phenotyping and subphenotyping have gained prominence in the most syndromic and heterogenous critical care disease states, acute respiratory distress syndrome and sepsis. Grouping of patients has been informative across a spectrum of clinically observable physiological parameters, biomarkers, and genomic data. Bayesian thinking and phenotyping are emerging as elements of adaptive clinical trials and predictive enrichment, paving the way for a new era of high-quality evidence. These concepts share a common goal, sifting through the noise of heterogeneity in critical care to increase the value of existing and future research. The future of critical care medicine will inevitably involve modification of statistical methods through Bayesian analyses and targeted therapeutics via phenotyping. Clinicians must be familiar with these systems that support recommendations to improve decision-making in the gray areas of critical care practice.

Scalar bounded-from-below conditions from Bayesian active learning

Scalar bounded-from-below conditions from Bayesian active learning

Non-linear association and benchmark dose of blood pressure on carotid artery intima-media thickening in a general population of southern China

Background and aimsThis study aimed to evaluate whether there is a J-curve association between blood pressure (BP) and carotid artery intima-media thickening (CAIT) and estimate the effect of the turning point of BP on CAIT.Methods and resultsData from 111,494 regular physical examinations conducted on workers and retirees (aged 18 years or older) between January 2011 and December 2016, exported from the hospital information system, were analyzed. Restricted cubic splines (RCS) logistic regression was employed to access the association of BP with CAIT, and Bayesian benchmark dose methods were used to estimate the benchmark dose as the departure point of BP measurements. All the pnon-linear values of BP measurements were less than 0.05 in the RCS logistic regression models. Both systolic blood pressure (SBP) and diastolic blood pressure (DBP) had J-curve associations with the risk of CAIT at a turning point around 120/70 mmHg in the RCS. The benchmark dose for a 1% change in CAIT risk was estimated to be 120.64 mmHg for SBP and 72.46 mmHg for DBP.ConclusionThe J-curve associations between SBP and DBP and the risk of CAIT were observed in the general population in southern China, and the turning point of blood pressure for significantly reducing the risk of CAIT was estimated to be 120.64/72.46 mmHg for SBP/DBP.

Spike and slab Bayesian sparse principal component analysis

Spike and slab Bayesian sparse principal component analysis

Bayesian maximum entropy interpolation analysis for rapid assessment of seismic intensity using station and ground motion prediction equations

In this paper, we explored the combination of seismic station data and ground motion prediction equations (GMPE) to predict seismic intensity results by using Bayesian Maximum Entropy (BME) method. The results indicate that: 1) In earthquake analysis in Japan, soft data has predicted higher values of intensity in disaster areas. BME corrected this phenomenon, especially near the epicenter. Meanwhile, for earthquakes in the United States, BME corrected the erroneous prediction of rupture direction using soft data. 2) Compared with other spatial interpolation methods, the profile results of Japan earthquake and Turkey earthquake show that BME is more consistent with ShakeMap results than IDW and Kriging. Moreover, IDW has a low intensity anomaly zone. 3) The BME method overcomes the phenomenon that the strength evaluation results do not match the actual failure situation when the moment magnitude is small. It more accurately delineates the scope of the disaster area and enriches the post-earthquake processing of disaster area information and data. BME has a wide range of applicability, and it can still be effectively used for interpolation analysis when there is only soft data or few sites with data available.

Terrestriality across the primate order: A review and analysis of ground use in primates.

Terrestriality is relatively rare in the predominantly arboreal primate order. How frequently, and when, terrestriality appears in primate evolution, and the factors that influence this behavior, are not well understood. To investigate this, we compiled data describing terrestriality in 515 extant nonhuman primate taxa. We describe the geographic and phylogenetic distribution of terrestriality, including an ancestral state reconstruction estimating the frequency and timing of evolutionary transitions to terrestriality. We review hypotheses concerning the evolution of primate terrestriality and test these using data we collected pertaining to characteristics including body mass and diet, and ecological factors including forest structure, food availability, weather, and predation pressure. Using Bayesian analyses, we find body mass and normalized difference vegetation index are the most reliable predictors of terrestriality. When considering subsets of taxa, we find ecological factors such as forest height and rainfall, and not body mass, are the most reliable predictors of terrestriality for platyrrhines and lemurs.

On the Initial Phase of the Ongoing Unrest at Campi Flegrei and Its Relation with Subsidence at Vesuvio (Italy)

The densely inhabited area of Naples (Italy), between the Campi Flegrei and Vesuvio volcanoes, is one of the most hazardous regions in the world. After two decades of sustained subsidence, Campi Flegrei has been experiencing an accelerating uplift since 2005. The uplift is currently associated with unusual seismicity and increased degassing. To try to identify the cause of the shift from subsidence to uplift and explore any connection between Campi Flegrei and Vesuvio, we analysed the ground displacement time series of the two volcanoes from 1993 to 2010, obtained from ERS/ENVISAT Synthetic Aperture Radar imagery. To distinguish between the various sources of deformation, we used simple scatter plots and a blind source separation technique called variational Bayesian independent component analysis (vbICA). We obtained consistent results using both approaches. Specifically, with vbICA, we identified two significant independent components (ICs). IC1 describes the subsidence that occurred at Campi Flegrei prior to 2000, including the mini-uplifts of 2000 and 2005, and part of the post-2005 uplift. The expansion and contraction of two volumes beneath Campi Flegrei satisfy IC1: a sill-shaped volume at a depth of approximately 3 km and a small volume at a depth of 1–2 km, respectively. The two sources of deformation reproduce the large-scale deformation in the Campi Flegrei area and the local deformation in the Solfatara area, respectively. In the Campi Flegrei area, IC2 exhibits primarily uplift, which is concentrated in the eastern part of the caldera. The deformation pattern is complex and difficult to interpret. If we model it using simple spheroidal deformation sources, the pattern suggests that two volumes at depths of approximately 9 and 8 km are experiencing opposite activity, namely contraction (beneath the southwestern part of the caldera) and expansion (beneath the central part of the caldera). In the Vesuvio area, IC2 is consistent with the deformation induced by the contraction of a volume at a depth of around 9 km. The contraction beneath Vesuvio is smaller in magnitude than the expansion/contraction beneath Campi Flegrei. The correlation observed after 2002 between uplift at Campi Flegrei and subsidence at Vesuvio suggests the transfer of magma and/or magmatic fluids between the two plumbing systems at 8–9 km depth. This implies that part of the ongoing unrest at Campi Flegrei may have been promoted by mass transfer from below Vesuvio.

Comparison of some Bayesian estimation methods for type-I generalized extreme value distribution with simulation

The Weibull distribution is considered one of the Type-I Generalized Extreme Value (GEV) distribution, and it plays a crucial role in modeling extreme events in various fields, such as hydrology, finance, and environmental sciences. Bayesian methods play a strong, decisive role in estimating the parameters of the GEV distribution due to their ability to incorporate prior knowledge and handle small sample sizes effectively. In this research, we compare several shrinkage Bayesian estimation methods based on the squared error and the linear exponential loss functions. They were adopted and compared by the Monte Carlo simulation method. The performance of these methods is assessed based on their accuracy and computational efficiency in estimating the scale parameter of the Weibull distribution. To evaluate their performance, we generate simulated datasets with different sample sizes and varying parameter values. A technique for pre-estimation shrinkage is suggested to enhance the precision of estimation. Simulation experiments proved that the Bayesian shrinkage estimator and shrinkage pre-estimation under the squared loss function method are better than the other methods because they give the least mean square error. Overall, our findings highlight the advantages of shrinkage Bayesian estimation methods for the proposed distribution. Researchers and practitioners in fields reliant on extreme value analysis can benefit from these findings when selecting appropriate Bayesian estimation techniques for modeling extreme events accurately and efficiently.

Analysis of the Associations of Measurements of Body Composition and Inflammatory Factors with Cardiovascular Disease and Its Comorbidities in a Community-Based Study

The associations of cardiovascular disease (CVD) with comorbidities and biochemical and body composition measurements are repeatedly described but have not been studied simultaneously. In the present cross-sectional study, information on CVD and comorbidities [type 2 diabetes mellitus (T2DM), hypertension (HTN), and hyperlipidemia (HDL)], body composition, levels of soluble markers, and other measures were collected from 1079 individuals. When we examined the association of each comorbidity and CVD, controlling for other comorbidities, we observed a clear pattern of the comorbidity-related specific associations with tested covariates. For example, T2DM was significantly associated with GDF-15 levels and the leptin/adiponectin (L/A) ratio independently of two other comorbidities; HTN, similarly, was independently associated with extracellular water (ECW) levels, L/A ratio, and age; and HDL was independently related to age only. CVD showed very strong independent associations with each of the comorbidities, being associated most strongly with HTN (OR = 10.89, 6.46–18.38) but also with HDL (2.49, 1.43–4.33) and T2DM (1.93, 1.12–3.33). An additive Bayesian network analysis suggests that all three comorbidities, particularly HTN, GDF-15 levels, and ECW content, likely have a main role in the risk of CVD development. Other factors, L/A ratio, lymphocyte count, and the systemic inflammation response index, are likely indirectly related to CVD, acting through the comorbidities and ECW.

Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control

The accuracy of the underlying model predictions is crucial for the success of model predictive control (MPC) applications. If the model is unable to accurately analyze the dynamics of the controlled system, the performance and stability guarantees provided by MPC may not be achieved. Learning-based MPC can learn models from data, improving the applicability and reliability of MPC. This study develops a nonlinear sparse variational Bayesian learning based MPC (NSVB-MPC) for nonlinear systems, where the model is learned by the developed NSVB method. Variational inference is used by NSVB-MPC to assess the predictive accuracy and make the necessary corrections to quantify system uncertainty. The suggested approach ensures input-to-state stability (ISS) and the feasibility of recursive constraints in accordance with the concept of an invariant terminal region. Finally, a PEMFC temperature control model experiment confirms the effectiveness of the NSVB-MPC method.

Cosmic dipoles of active galactic nuclei at optical and radio wavelengths display much larger amplitudes than the cosmic microwave background dipole

Abstract Sky distributions of large samples of distant active galactic nuclei (AGNs) have shown dipoles significantly larger than the cosmic microwave background (CMB) dipole. However, a recent Bayesian analysis of the QUAIA sample, comprising 1.3 million quasars, has yielded a dipole that seems to be in tandem with the CMB dipole, in contravention of most previous studies of AGN dipoles. Since the question has large cosmological implications, we investigate the QUAIA quasar sample afresh, by directly computing the dipole from asymmetries observed in the source number counts. We instead find a dipole 3-4 times as large as the CMB dipole though in the same direction. Further, it has been claimed elsewhere that the difference between the CMB dipole and the radio dipole estimated from the NRAO VLA Sky Survey (NVSS), the first large catalogue that showed an AGN dipole about four times larger than the CMB dipole, can be fully accounted for by incorporating the shot-noise and clustering contributions to the total NVSS dipole. A careful reinvestigation of the NVSS dipole, however, shows that the random phenomena like shot noise or clustering cannot account for the actually observed NVSS asymmetries, which show a systematic dipole pattern over the sky.

Bayesian analysis of experimental and observational data: a review and illustration of the BANOVA R package

Bayesian analysis of experimental and observational data: a review and illustration of the BANOVA R package

Application of non parametric Bayesian methods in high dimensional data

With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc.

Novel non-linear models for clinical trial analysis with longitudinal data: A tutorial using SAS for both frequentist and Bayesian methods.

Longitudinal data from clinical trials are commonly analyzed using mixed models for repeated measures (MMRM) when the time variable is categorical or linear mixed-effects models (ie, random effects model) when the time variable is continuous. In these models, statistical inference is typically based on the absolute difference in the adjusted mean change (for categorical time) or the rate of change (for continuous time). Previously, we proposed a novel approach: modeling the percentage reduction in disease progression associated with the treatment relative to the placebo decline using proportional models. This concept of proportionality provides an innovative and flexible method for simultaneously modeling different cohorts, multivariate endpoints, and jointly modeling continuous and survival endpoints. Through simulated data, we demonstrate the implementation of these models using SAS procedures in both frequentist and Bayesian approaches. Additionally, we introduce a novel method for implementing MMRM models (ie, analysis of response profile) using the nlmixed procedure.

Bayesian Inference for Multiple Datasets

Estimating parameters for multiple datasets can be time consuming, especially when the number of datasets is large. One solution is to sample from multiple datasets simultaneously using Bayesian methods such as adaptive multiple importance sampling (AMIS). Here, we use the AMIS approach to fit a von Mises distribution to multiple datasets for wind trajectories derived from a Lagrangian Particle Dispersion Model driven from 3D meteorological data. A posterior distribution of parameters can help to characterise the uncertainties in wind trajectories in a form that can be used as inputs for predictive models of wind-dispersed insect pests and the pathogens of agricultural crops for use in evaluating risk and in planning mitigation actions. The novelty of our study is in testing the performance of the method on a very large number of datasets (>11,000). Our results show that AMIS can significantly improve the efficiency of parameter inference for multiple datasets.

Hidden Variable Models in Text Classification and Sentiment Analysis

In this paper, we are proposing extensions to the multinomial principal component analysis (MPCA) framework, which is a Dirichlet (Dir)-based model widely used in text document analysis. The MPCA is a discrete analogue to the standard PCA (it operates on continuous data using Gaussian distributions). With the extensive use of count data in modeling nowadays, the current limitations of the Dir prior (independent assumption within its components and very restricted covariance structure) tend to prevent efficient processing. As a result, we are proposing some alternatives with flexible priors such as generalized Dirichlet (GD) and Beta-Liouville (BL), leading to GDMPCA and BLMPCA models, respectively. Besides using these priors as they generalize the Dir, importantly, we also implement a deterministic method that uses variational Bayesian inference for the fast convergence of the proposed algorithms. Additionally, we use collapsed Gibbs sampling to estimate the model parameters, providing a computationally efficient method for inference. These two variational models offer higher flexibility while assigning each observation to a distinct cluster. We create several multitopic models and evaluate their strengths and weaknesses using real-world applications such as text classification and sentiment analysis.

Spatial–temporal transmission dynamics of HIV-1 CRF01_AE in Indonesia

Human immunodeficiency virus type 1 (HIV-1) remains a serious health threat in Indonesia. In particular, the CRF01_AE viruses were the predominant HIV-1 strains in various cities in Indonesia. However, information on the dynamic transmission characteristics and spatial–temporal transmission of HIV-1 CRF01_AE in Indonesia is limited. Therefore, the present study examined the spatial–temporal transmission networks and evolutionary characteristics of HIV-1 CRF01_AE in Indonesia. To clarify the epidemiological connection between CRF01_AE outbreaks in Indonesia and the rest of the world, we performed phylogenetic studies on nearly full genomes of CRF01_AE viruses isolated in Indonesia. Our results showed that five epidemic clades, namely, IDN clades 1–5, of CRF01_AE were found in Indonesia. To determine the potential source and mode of transmission of CRF01_AE, we performed Bayesian analysis and built maximum clade credibility trees for each clade. Our study revealed that CRF01_AE viruses were commonly introduced into Indonesia from Southeast Asia, particularly Thailand. The CRF01_AE viruses might have spread through major pandemics in Asian countries, such as China, Vietnam, and Laos, rather than being introduced directly from Africa in the early 1980s. This study has major implications for public health practice and policy development in Indonesia. The contributions of this study include understanding the dynamics of HIV-1 transmission that is important for the implementation of HIV disease control and prevention strategies in Indonesia.

Bayesian Network Analysis of Industrial Accident Risk for Fishers on Fishing Vessels Less Than 12 m in Length

The Marine Stewardship Council estimates that approximately 38 million people worldwide work in fisheries, and more than one-third of the global population is dependent on aquatic products for protein, highlighting the importance of sustainable fisheries. The FISH Safety Foundation reports that 300 fishers die every day. To achieve sustainable fisheries as a primary industry, the safety of human resources is of the utmost importance. The International Maritime Organization (IMO) and the International Labor Organization (ILO) have made efforts towards this goal, including the issuance of agreements and guidelines to reduce industrial accidents among fishing vessel workers. The criterion for applying these guidelines is usually a total ship length ≥12 m or ≥24 m. However, a vast majority of registered fishing vessels are <12 m long, and the fishers of these vessels suffer substantially more industrial accidents. Thus, we conducted a quantitative analysis of 1093 industrial accidents affecting fishers on fishing vessels <12 m in length, analyzed risk using a Bayesian network analysis (a method proposed by the Formal Safety Assessment of the IMO), and administered a questionnaire survey to a panel of experts in order to ascertain the risk for different types of industrial accidents and propose specific measures to reduce this risk.