Bayesian Inference Research Articles

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.

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.

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.

Pseudochroaptomus ecuadoriensis—a new genus and species of Philonthina and its systematic placement (Coleoptera: Staphylinidae: Staphylininae)

Pseudochroaptomus ecuadoriensis Chani-Posse et al., a new genus and species of the subtribe Philonthina (tribe Staphylinini) from the Tropical Andes of Ecuador, is described and illustrated. A dataset of 35 morphological characters scored for 23 taxa representing main clades of Philonthina, with focus on the Neotropical lineage and its subclades was prepared and analysed by maximum parsimony and Bayesian inference. Both analytical methods unambiguously placed Pseudochroaptomus as the sister-group of Chroaptomus Sharp.

Characterization of the Complete Mitochondrial Genome of Salassa thespis (Lepidoptera: Saturniidae) and Comparison with Other Lepidoptera Species

There are many species of Lepidoptera, but few complete mitochondrial genomes of Lepidoptera have been included in databases. Here, the complete mitochondrial genome sequence of Salassa thespis was isolated and characterized. It was 15,302 bp in length and contained 13 protein-coding genes (PCGs), two rRNA genes, 22 tRNA genes and an A + T-rich region. Among the 13 PCGs, the initiation codon of cytochrome c oxidase subunit 1 (cox1) was CGA, and the rest were ATN. The cox1 and cox2 genes had an incomplete stop codon T, while the rest terminated with TAA. Codon usage analysis showed that Phe, Ile, Leu and Asn were the most frequent amino acids, while Trp was the least. Like other Lepidopterans, some conserved motifs were found in the A + T-rich region, including a 17 bp poly-T guided by ATAGA, the AT-rich area and a poly-A element. Bayesian inference and maximum likelihood phylogenetic tree analysis based on 13 PCGs of S. thespis confirmed that it belonged to the Saturniidae family and showed the following relationship: (S. thespis + (Rhodinia fugax + Samia canningi)). The enrichment of mitochondrial DNA provides reference information for the study of the evolution and diversity of Lepidoptera insects.

The R2D2 prior for generalized linear mixed models

The R2D2 prior for generalized linear mixed models

Prior effective sample size for exponential family distributions with multiple parameters

AbstractThe setting of priors is an important issue in Bayesian analysis. In particular, when external information is applied, a prior with too much information can dominate the posterior inferences. To prevent this effect, the effective sample size (ESS) can be used. Various ESSs have been proposed recently; however, all have the problem of limiting the applicable prior distributions. For example, one ESS can only be used with a prior that can be approximated by a normal distribution, and another ESS cannot be applied when the parameters are multidimensional. We propose an ESS to be applied to more prior distributions when the sampling model belongs to an exponential family (including the normal model and logistic regression models). This ESS has the predictive consistency and can be used with multidimensional parameters. It is confirmed from normally distributed data with the Student's‐t priors that this ESS behaves as well as an existing predictively consistent ESS for one‐parameter exponential families. As examples of multivariate parameters, ESSs for linear and logistic regression models are also discussed.

Exposure to synthesized tribromobisphenol A and critical effects: Metabolic pathways, disease signature, and benchmark dose derivation

2,2′,6-Tribromobisphenol A (Tri-BBPA), the main debrominated congener of tetrabromobisphenol A (TBBPA), is ubiquitous in the environment and human body but with unknown toxicity. Tri-BBPA was synthesized and applied to investigate its sub-chronic exposure effects on 28 organ coefficients and clinical health indicators related to liver function, kidney function, and cardiovascular system function in female mice. Results showed that the liver was the targeted organ of Tri-BBPA exposure. Compared to the control group, the changes in liver coefficient, cholinesterase, total protein, albumin, γ-glutamyl transpeptidase, lactate dehydrogenase, and creatine kinase levels ranged from −61.2 % to 35.5 % in the high-exposed group. Creatine kinase was identified as a critical effect indicator of Tri-BBPA exposure. Using the Bayesian benchmark dose derivation method, a lower reference dose than TBBPA was established for Tri-BBPA (10.6 μg/kg-day). Serum metabolomics revealed that Tri-BBPA exposure may primarily damage the liver by disrupting tryptophan metabolism related to L-alanine, tryptamine, 5-hydroxyindoleacetic acid, and 5-methoxyindoleacetate in liver cells and leading to liver dysfunction. Notably, epilepsy, schizophrenia, early preeclampsia, and late-onset preeclampsia were the top six enriched diseases, suggesting that the nervous system may be particularly affected by Tri-BBPA exposure. Our findings hinted a non-negligible health risk of exposure to debrominated products of TBBPA.

Graph-Aligned Random Partition Model (GARP)

Bayesian nonparametric mixtures and random partition models are powerful tools for probabilistic clustering. However, standard independent mixture models can be restrictive in some applications such as inference on cell lineage due to the biological relations of the clusters. The increasing availability of large genomic data requires new statistical tools to perform model-based clustering and infer the relationship between homogeneous subgroups of units. Motivated by single-cell RNA data we develop a novel dependent mixture model to jointly perform cluster analysis and align the clusters on a graph. Our flexible graph-aligned random partition model (GARP) exploits Gibbs-type priors as building blocks, allowing us to derive analytical results for the probability mass function (pmf) on the graph-aligned random partition. We derive a generalization of the Chinese restaurant process from the pmf and a related efficient and neat MCMC algorithm to implement Bayesian inference. We illustrate posterior inference under the GARP using single-cell RNA-seq data from mice stem cells. We further investigate the performance of the model in recovering the underlying clustering structure as well as the underlying graph by means of simulation studies.

Mass-redshift dependency of Supermassive Black Hole Binaries for the Gravitational Wave Background

Abstract Studying how the black hole (BH) - (galaxy) bulge mass relation evolves with redshift provides valuable insights into the co-evolution of supermassive black holes and their host galaxies. However, obtaining accurate measurement of BH masses is challenging due to the bias towards the most massive and luminous galaxies. Instead we focus on the BH and bulge masses as they vary with redshift using the EAGLE, Illustris, TNG100, TNG300, Horizon-AGN and SIMBA large-scale cosmological simulations. We use an analytical astrophysical model with galaxy stellar mass function, pair fraction, merger timescale and BH-bulge mass relation extended to include redshift evolution. The model can predict the intensity of the gravitational wave background (GWB) produced by a population of supermassive black hole binary (SMBHB) as a function of the frequency. This allows us to compare the predictions of this model with the constraints of Pulsar Timing Array observations. Here, we employ Bayesian analysis for the parameter inference. We find that all six simulations are consistent ≤3.5σ with a range of simulated GWB spectra. By fixing the BH-bulge mass parameters to the simulations we analyze the changes in the constraints on the other astrophysical parameters. Furthermore, we also examine the variation in SMBHB merger rate with mass and redshift between these large-scale simulations.

Simultaneous but independent spatial associations for pitch and loudness.

For the auditory dimensions loudness and pitch a vertical SARC effect (Spatial Association of Response Codes) exists: When responding to loud (high) tones, participants are faster with top-sided responses compared to bottom-sided responses and vice versa for soft (low) tones. These effects are typically explained by two different spatial representations for both dimensions with pitch being represented on a helix structure and loudness being represented as spatially associated magnitude. Prior studies show incoherent results with regard to the question whether two SARC effects can occur at the same time as well as whether SARC effects interact with each other. Therefore, this study aimed to investigate the interrelation between the SARC effect for pitch and the SARC effect for loudness in a timbre discrimination task. Participants (N = 36) heard one tone per trial and had to decide whether the presented tone was a violin tone or an organ tone by pressing a top-sided or bottom-sided response key. Loudness and pitch were varied orthogonally. We tested the occurrence of SARC effects for pitch and loudness as well as their potential interaction by conducting a multiple linear regression with difference of reaction time (dRT) as dependent variable, and loudness and pitch as predictors. Frequentist and Bayesian analyses revealed that the regression coefficients of pitch and loudness were smaller than zero indicating the simultaneous occurrence of a SARC effects for both dimensions. In contrast, the interaction coefficient was not different from zero indicating an additive effect of both predictors.

Environmental Influences on the Drying Rate of Spray Applied Disinfestants from Horticultural Production Surfaces

Drying rates of disinfestants commonly applied to horticultural plant production surfaces were evaluated under cool to hot weather and under laboratory conditions to characterize the range of drying times and how this relates to contact times specified on product labels. Drying rates of six disinfestants [isopropyl alcohol (IPA), two quaternary ammonium compounds (QACs), two peroxygen compounds (PXs), sodium hypochlorite (bleach)] and water were evaluated when applied to six substrate materials (concrete, galvanized metal, polypropylene ground fabric, polyethylene plastic sheet, pressure-treated pine, and twin-wall clear polycarbonate) based on the weather variables of solar radiation, temperature, and relative humidity. Differences were evident at the point of application. Disinfestants with low (IPA, both QACs and one PX) and high (bleach, one PX and water) surface tension provided approximately 100% and 60% coverage, respectively, when applied to horizontal, non-porous solid materials. Disinfestants applied to horizontal porous materials (concrete, fabric and wood) persisted on the surface for a mean of only 9 to 113 sec because solutions were actively drawn into the substrates internal structure. Disinfestants applied to vertical twin-wall greenhouse material flowed off, while retaining only a maximum beaded wetness coverage of 14%. A Bayesian analysis procedure was used to model drying effects of disinfestants correlated with substrate and weather variables based on posterior marginal and prediction trends. Generally, the fastest drying rate occurred in the first 2.5 min, and approximately 50% of coverage was retained by 5 min. The evaporative process was variable with distinct interactions occurring among the experimental variables.