Bayesian Analysis Method Research Articles

Genetic Features of the Bream Abramis brama from the Lake System of the Sebezhsky National Park

For the first time, the analysis of the genetic variability of microsatellite DNA for five loci in the bream Abramis brama (L.) from lakes Sebezhskoye and Necheritsa of the Sebezhsky National Park, Sebezhsky district of the Pskov region, included in the lake system, was carried out. The results obtained demonstrate the uniformity of estimates of the genetic variability of bream in the lake system. Estimates of the allelic diversity of microsatellite loci and the expected heterozygosity of local bream groups did not differ significantly. The total genetic differentiation of bream was θ = 0.004. 95% CI (-0.01; 0.01) and was significantly insignificant. The population-genetic structure based on the studied multilocus genotypes has not been revealed by the Bayesian analysis method. The result indicate a high level of gene flow between local bream groupings and make it possible to assume the presence of a genetically unified panmixed population in the Sebezh lakes system.

Shale Fracture Static Stability Evaluation Method Based on 3D Discrete Fracture Model: A Case Study on the Luzhou Area in Southern Sichuan Basin, SW China

Abstract The stress interference in deep shale may lead to shale fracturing and instability and consequently results in engineering risks such as casing deformation in horizontal wells. Therefore, in the early stages of shale gas exploration and development, it is of great significance to evaluate the stability of shale fractures in advance. The stability evaluation of shale reservoir faults can be mainly divided into two categories at present: quantitative evaluation of single faults and qualitative evaluation of wide-range faults. The quantitative evaluation of single faults primarily relies on numerical simulation techniques, while the qualitative evaluation of wide-range faults primarily employs geophysical methods. Both methods are difficult to meet the needs of shale gas exploration and development. The shale fracture stability evaluation method based on 3D discrete fracture model which is developed in this article can quantitatively evaluate the static stability of wide-range shale fractures in the early stage of exploration and development. In this article, seismic geometric attributes were calculated, and fracture seismic facies was established by means of the Bayesian probabilistic cluster analysis method. Then, 3D discrete fracture modeling under the constraint of fracture seismic facies was performed to establish a discrete fracture model. Finally, according to the Mohr–Coulomb criterion, the shear stress and effective normal stress of discrete fracture were calculated by using the formation pressure data in other exploration and development results and the regional in situ stress obtained through seismic prestack inversion. And thus, the stability evaluation of the fractures in shale reservoirs was eventually completed. In addition, this method was verified by using the casing deformation points in some actual shale-gas horizontal wells, the actual small fault points, and microseismic data. It indicates that the method has higher accuracy and is better applicable to the early shale gas exploration and development.

A novel Bayesian updating finite element model using enhanced unscented Kalman filter for time-dependent estimation of rockfill dam structure deformation

Due to the time-dependent effect of rockfill dams, the conventional time-invariant finite element method (FEM) can hardly meet practical engineering requirements. This paper proposes an updating Bayesian FEM method for accurate long-term deformation analysis. A combined FEM model is introduced accounting for both instantaneous and creep behaviors. The FEM model is then updated using a Bayesian algorithm, unscented Kalman filter (UKF). The UKF calibrates the prior FEM predictions by incorporating real-time measurement data, thus iteratively reducing discrepancies between model predictions and actual observations. To further enhance the algorithm accuracy, a power-law-based fading memory factor is proposed to mitigate measurement noise in standard UKF. For parameter identification, a slice approach of the high-dimensional covariance confidence ellipsoid is developed. The methodology is validated in Qingyuan rockfill dam, in Guangdong province, China. Results show that the updated FEM is more consistent with the actual monitoring data. The fading memory improves standard UKF performance with a lower relative root-mean-square error (RRMSE). Additionally, the slice method reveals that a specific three-parameter configuration behaves better than the others. The proposed approach can also be extended to other fields including slope and tunneling.

Gamma-mixture Bayesian method for anomalous coalmine pressure analysis

Gamma-mixture Bayesian method for anomalous coalmine pressure analysis

Use of Bayesian probabilistic model approach in common bean varietal recommendation

AbstractRecommendation of new varieties is supported by value for cultivation and use (Valor de Cultivo e Uso [VCU]) trials. For a more reliable recommendation, it is necessary to identify methodologies that make better use of the genotype‐by‐environment interaction (GEI). The methodology proposed by Dias et al. is an alternative to take advantage of the GEI; it considers concepts of Bayesian models and probability methods of adaptation and stability analysis in a single model, classifying the genotypes regarding possible success based on a defined selection intensity. Thus, the aim of the present study was to explore the use of Bayesian probabilistic method for the purpose of recommend common bean (Phaseolus vulgaris L.) varieties. To that end, we used grain yield data from 15 genotypes of common bean evaluated in 42 environments distributed over different crop seasons, years, and locations in regard to VCU trials conducted from 2016 to 2018. Under a predefined selection intensity of 30%, the genotypes with greater marginal probability of superior performance were G01, G14, G07, G11, and G02. The genotypes with greater marginal probability of superior stability were G06, G07, G04, G03, and G12. Considering the joint probability of superior performance and yield stability, the genotypes G07, G14, G01, G11, and G04 stand out. Therefore, the use of the Bayesian probabilistic method showed promise in recommendation of common bean varieties.

Multi-Objective Model Updating of Tower Structures Featuring Closely Spaced Modes

In this contribution, we present a multi-objective model updating approach for the purpose of damage localisation and quantification on a girder mast structure based on closely spaced modes. In structural health monitoring (SHM), modal quantities, such as eigenfrequencies and mode shapes, serve as monitoring parameters. Specifically, model updating strategies adjust the local structural stiffness of a simulated structure to reduce the difference between the simulation results and the experimentally identified modal quantities. The resulting structural stiffness represents the damaged state of the structure, thus providing valuable information about the occurred damage. Symmetrical tower structures are challenging for model updating approaches due to their closely spaced bending modes, which are prone to uncertain alignments between the identified mode shapes and the model mode shapes. As a result, the naive comparison between the mode shapes of the real structure and the model often yields poor results. In order to overcome this problem, we adapt the concept of the subspace of order 2 modal assurance criterion (S2MAC), i.e., finding the best fit to a mode shape vector in a subspace spanned by two bending modes, hence enabling a meaningful comparison of the changes in the dominant mode shapes. The proposed method is validated using an experimental 9 m tall girder mast structure featuring reversible damage mechanisms. Mode shapes and eigenfrequencies, identified using the Bayesian operational modal analysis (BayOMA) method, are employed for multi-objective model updating, allowing for the estimation of several Pareto-optimal solutions. The structure is parameterised with two damage parameters, accounting for the location and severity, which limits the optimisation to only one damaged beam at any given time. The damage localisation is performed for data sets recorded over a time span of several months, including three different damage positions, resulting in a successful validation of the proposed

A Bayesian high-dimensional mediation analysis for multilevel genome-wide epigenetic data

Causal mediation analysis has increasingly become a popular practice in various clinical trials and epidemiological applications to evaluate whether an intermediate variable is on the pathway between the exposure of interest and a response. Previous mediation analyses in the literature mainly focused on settings with a single or low-dimensional mediators and single-level data. In this article, we propose a Bayesian causal mediation analysis method that can handle our multilevel intergenerational epigenetic mechanisms study (IEMS) with high-dimensional mediators. Specifically, we develop a Bayesian hierarchical model for data with such complexity, and then employ the Bayesian spike-and-slab priors on the exposure-mediator-outcome effect pathway to identify active mediators involved in mediation. We derive the natural indirect and direct effects based on our hierarchical model and provide statistical inference based on Markov chain Monte Carlo (MCMC) methods. Our simulation study demonstrates that our proposed Bayesian method outperforms other alternative methods in various scenarios. We further illustrate the utility of our method to IEMS to assess the causal mechanisms between maternal exposure to climate extremes and offspring's growth outcomes through DNA methylation.

Pancreatitis with use of new diabetic medications: a real-world data study using the post-marketing FDA adverse event reporting system (FAERS) database.

Background: Pancreatitis is characterized by inflammation of the pancreas and significantly affects quality of life. Less than 5% of pancreatitis cases are drug-induced, but recent evidence suggests a substantial risk associated with glucagon-like peptide-1 receptor agonists (GLP-1 RAs). The aim of this study was to compare the risk of developing pancreatitis between those using GLP-1 RAs and those using sodium-glucose transport protein 2 (SGLT2) inhibitors and dipeptidyl peptidase 4 (DPP-4) inhibitors. Methods: This study was done using the FDA Adverse Event Reporting System (FAERS) database from 2019 to 2021. This database contains information from diverse submissions from healthcare providers, patients, and manufacturers. To ensure fairness and accuracy, the risk of pancreatitis associated with other hypoglycemic agents (SGLT2 inhibitors and DPP-4 inhibitors) was also investigated. Traditional and Bayesian statistical analysis methods were used to identify disproportionate statistics and included the reporting odds ratio (ROR), proportional reporting ratio (PRR), empirical Bayes geometric mean (EBGM), and information component (IC). A drug-adverse-event combination that met the criteria of all four indices was deemed a signal. Results: The analysis of 2,313 pancreatitis reports linked to hypoglycemic agents revealed a predominant association with GLP-1 RA (70.2%) compared to DPP-4 inhibitors (15%) and SGLT2 (14.7%). Most of these reports involved female patients (50.4%), and the highest incidence occurred in those over 50 years old (38.4%). Additionally, 17.7% of the reports were associated with serious events. The ROR was significant for the risk of pancreatitis when using DPP-4 (13.2, 95% confidence interval (CI) 11.84-14.70), while the ROR for GLP-1 was 9.65 (95% CI 9.17-10.16). The EBGM was highest with DPP-4 (12.25), followed by GLP-1 (8.64), while IC was highest with DPP-4 inhibitors (3.61). Liraglutide had the greatest association with pancreatitis among the GLP-1 RAs (ROR: 6.83, 95% CI 6.60-7.07). Conclusion: The findings show that pancreatitis has a strong link with DPP-4 inhibitors and GPL1 agonists, which pose a greater risk. Among the GLP-1 agonist medications, liraglutide has been found to have an association with pancreatitis.

Phylogenetic Analysis of Rare and Endangered Tulipa Species (Liliaceae) of Kazakhstan Based on Universal Barcoding Markers.

In Kazakhstan, the genus Tulipa is represented by 35 species, 18 of which are listed in the Red Data Book of Kazakhstan and protected by the state. Recent studies of tulip specimens from regions bordering Kazakhstan emphasize the significance of species inventory and report the discovery of several hybrids. In this study, eight tulip species were identified based on morphological characteristics and using DNA barcoding methods. Molecular genetic markers, including nrDNA (ITS) and cpDNA markers (rbcL, matK), of the studied species were sequenced and analyzed using the Bayesian inference and maximum likelihood phylogenetic analysis methods. Our work demonstrates that DNA barcodes based on the ITS, rbcL, and matK marker regions have successful practical applicability, with ITS being the most informative at the intragenic level. However, for distinguishing closely related taxa, the most effective approach would be to use a combined dataset of sequences from multiple DNA markers. The results showed discrepancies in the placement of several taxa (T. kaufmanniana, T. patens), likely due to introgression and natural spontaneous hybridization. The molecular phylogenetic analysis suggests the existence of a previously undescribed hybrid between T. patens and T. alberti. Further detailed population studies are needed to validate this hypothesis.

Relationship between Anaplastic Lymphoma Kinase Inhibitors and Epileptic Seizure Disorder: A Post-Marketing Surveillance Study

Introduction: Anaplastic lymphoma kinase (ALK) has been to be involved in the uptake and regulation of dopamine 2 receptor (D2R), a G protein-coupled receptor expressed in various brain regions. Therefore, it is crucial to understand the relationship between ALK inhibitors and seizures is an important issue. This study investigated the relationship between ALK inhibitors and seizures. Methods: This study investigated the relationship between ALK inhibitors and seizures through a disproportionality analysis using the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The target drugs were the ALK inhibitors crizotinib, ceritinib, alectinib, brigatinib, and lorlatinib. The seizures covered were defined high-level group term (HLGT): “Seizures (incl. subtype)” including high-level term (HLT): “seizures and seizure disorders NEC.” This study used the information component (IC), a signal score, as a Bayesian statistical method for disproportionality analysis. The signal detection criteria used in this study were the same as those reported previously: a lower limit of 95% credible interval (CrI) for IC >0. Results: The signal scores of '“seizures and seizure disorders not elsewhere classified (NEC)” “for each ALK inhibitor were crizotinib (IC: −0.00052, 95% CrI: −0.38–0.27), ceritinib (IC: 1.18, 95% CrI: 0.68–1.54), alectinib (IC: 0.68, 95% CrI: 0.19–1.02), brigatinib (IC: 1.04, 95% CrI: 0.32–1.54), and lorlatinib (IC: 0.82, 95% CrI: 0.11–1.32). On the other hand, “generalized tonic-clonic seizures,” “partial simple seizures NEC,” “absence seizures,” and “partial complex seizures” had no or few reported cases, and no signal was detected. Conclusion: To our knowledge, this is the first report to evaluate the relationship between ALK inhibitors and seizures using post-marketing surveillance data. These results suggest that ceritinib, alectinib, brigatinib, and lorlatinib, which are highly brain-migrating drugs, are associated with seizures.

Spatial analysis of cholangiocarcinoma in relation to diabetes mellitus and Opisthorchis viverrini infection in Northeast Thailand

Cholangiocarcinoma (CCA) exhibits a heightened incidence in regions with a high prevalence of Opisthorchis viverrini infection, with previous studies suggesting an association with diabetes mellitus (DM). Our study aimed to investigate the spatial distribution of CCA in relation to O. viverrini infection and DM within high-risk populations in Northeast Thailand. Participants from 20 provinces underwent CCA screening through the Cholangiocarcinoma Screening and Care Program between 2013 and 2019. Health questionnaires collected data on O. viverrini infection and DM, while ultrasonography confirmed CCA diagnoses through histopathology. Multiple zero-inflated Poisson regression, accounting for covariates like age and gender, assessed associations of O. viverrini infection and DM with CCA. Bayesian spatial analysis methods explored spatial relationships. Among 263,588 participants, O. viverrini infection, DM, and CCA prevalence were 32.37%, 8.22%, and 0.36%, respectively. The raw standardized morbidity ratios for CCA was notably elevated in the Northeast’s lower and upper regions. Coexistence of O. viverrini infection and DM correlated with CCA, particularly in males and those aged over 60 years, with a distribution along the Chi, Mun, and Songkhram Rivers. Our findings emphasize the association of the spatial distribution of O. viverrini infection and DM with high-risk CCA areas in Northeast Thailand. Thus, prioritizing CCA screening in regions with elevated O. viverrini infection and DM prevalence is recommended.

Bayesian Back Analysis of Axially Loaded Pile Response Using Fibre Optic Strain Data

The load-transfer method has been extensively used to evaluate the deformation behavior of foundation piles, with the nonlinear load-settlement behavior of a pile-soil system modeled using load-transfer curves. Although load-transfer models have a considerable impact on the pile response, the model parameters are usually estimated empirically from soil parameters using transformation models, and the associated uncertainties are often neglected. Fiber optic sensing technology provides detailed axial strain information along the entire length of a pile; however, the full potential of such utilization has not yet been realized. This paper presents a Bayesian back analysis method for statistical descriptions of load-transfer curves and pile responses based on fiber optic measurement. A pile test case study is used to illustrate the proposed method, and several candidate model configurations are compared. The results show that the predicted pile response generally matches the measured data, and the quantified uncertainty allows for a better understanding of the possible ranges of the pile response, in addition to the single best-estimate result.

Bayesian Mediation Analysis with an Application to Explore Racial Disparities in the Diagnostic Age of Breast Cancer

A mediation effect refers to the effect transmitted by a mediator intervening in the relationship between an exposure variable and a response variable. Mediation analysis is widely used to identify significant mediators and to make inferences on their effects. The Bayesian method allows researchers to incorporate prior information from previous knowledge into the analysis, deal with the hierarchical structure of variables, and estimate the quantities of interest from the posterior distributions. This paper proposes three Bayesian mediation analysis methods to make inferences on mediation effects. Our proposed methods are the following: (1) the function of coefficients method; (2) the product of partial difference method; and (3) the re-sampling method. We apply these three methods to explore racial disparities in the diagnostic age of breast cancer patients in Louisiana. We found that African American (AA) patients are diagnosed at an average of 4.37 years younger compared with Caucasian (CA) patients (57.40 versus 61.77, p< 0.0001). We also found that the racial disparity can be explained by patients’ insurance (12.90%), marital status (17.17%), cancer stage (3.27%), and residential environmental factors, including the percent of the population under age 18 (3.07%) and the environmental factor of intersection density (9.02%).

New insights into the interactions between the gut microbiota and the inflammatory response to ulcerative colitisin a mouse model of dextran sodium sulfate and possible mechanisms of action for treatment with PE&AFWE.

Inflammatory bowel disease (IBD), comprising Crohn's disease (CD) and ulcerative colitis (UC), is a heterogeneous state of chronic intestinal inflammation. Intestinal innate immunity, including innate immune cells, defends against pathogens and excessive entry of gut microbiota, while preserving immune tolerance to resident intestinal microbiota, and may be characterized by its capacity to produce a rapid and nonspecific reaction. The association between microbiota dysbiosis and the pathogenesis of IBD is complex and dynamic. When the intestinal ecosystem is in dysbiosis, the reduced abundance and diversity of intestinal gut microbiota make the host more vulnerable to the attack of exogenous and endogenous pathogenic gut microbiota. The aim of our study was to comprehensively assess the relationship between microbial populations within UC, the signaling pathways of pathogenic gut microbe therein and the inflammatory response, as well as to understand the effects of using PE&AFWE (poppy extract [Papaver nudicaule L.] and Artemisia frigida Willd. extract) on UC modulation. A UC mouse model was established by inducing SPF-grade C57BL/6 mice using dextrose sodium sulfate (DSS). Based on metagenomic sequencing to characterize the gut microbiome, the relationship between gut microbiota dysbiosis and gut microbiota was further studied using random forest and Bayesian network analysis methods, as well as histopathological analysis. (1) We found that the 5 gut microbiota with the highest relative abundance of inflammatory bowel disease UC model gut microbiota were consistent with the top 5 ranked natural bacteria. There were three types of abundance changes in the model groups: increases (Chlamydiae/Proteobacteria and Deferribacteres), decreases (Firmicutes), and no significant changes (Bacteroidetes). The UC model group was significantly different from the control group, with 1308 differentially expressed species with abundance changes greater than or equal to 2-fold. (2) The proportion of the fecal flora in the UC group decreased by 37.5% in the Firmicutes and increased by 14.29% in the proportion of Proteobacteria compared to the control group before treatment. (3) The significantly enriched and increased signaling pathways screened were the 'arachidonic acid metabolic pathway' and the 'phagosomal pathway', which both showed a decreasing trend after drug administration. (4) Based on the causal relationship between different OTUs and the UC model/PE&AFWE administration, screening for directly relevant OTU networks, the UC group was found to directly affect OTU69, followed by a cascade of effects on OTU12, OTU121, OTU93, and OTU7, which may be the pathway of action that initiated the pathological changes in normal mice. (5) We identified a causal relationship between common differentially expressed OTUs and PE&AFWE and UC in the pre- and post-PE&AFWE-treated groups. Thereby, we learned that PE&AFWE can directly affect OTU90, after which it inhibits UC, inhibiting the activity of arachidonic acid metabolic pathway by affecting OTU118, which in turn inhibits the colonization of gut microbiota by OTU93 and OTU7. (6) Histopathological observation and scoring (HS) of the colon showed that there was a significant difference between the model group and the control group (p < 0.001), and that there was a significant recovery in both the sulfasalazine (SASP)and the PE&AFWE groups after the administration of the drug (p < 0.0001). We demonstrated causal effects and inflammatory metabolic pathways in gut microbiota dysbiosis and IBD, with five opportunistic pathogens directly contributing to IBD. PE&AFWE reduced the abundance of proteobacteria in the gut microbiota, and histopathology showed significant improvement.

Policy role, information disclosure, and enterprise innovation

The interactive relationship between policy, information disclosure and Enterprise innovation is crucial for the development of modern Enterprises. This paper examines how disclosure regulations imposed by regulators stimulate Enterprise innovation by shaping the dissemination of critical information that supports competitive advantage. This paper uses fixed effects and Bayesian model analysis methods for analysis. The study finds that policy and information disclosure drive corporate executives to make strategic choices, which will promote corporate innovation to ensure sustainable development and competitive advantage of enterprises.

Abstract 6244: MultiTME: A Bayesian method for integration and analysis of multimodal, multi-patient spatial profiling data

Abstract The role of the tumor microenvironment (TME) in cancer evolution and progression is complex and multi-faceted. Multiple cellular phenotypes and patterns of cell-cell communication have been implicated as drivers of drug resistance and metastasis. The spatial architecture of the TME is emerging as particularly important in characterizing the trajectory of tumor growth and response to therapy. Accordingly, there has been a rapid rise in technologies to spatially profile the TME. Each technology focuses on a different modality (e.g. transcriptomics, proteomics, metabolomics) and operates at a different granularity, from coarse-grained tissue spots aggregating dozens of cells to fine-grained subcellular resolution. There is currently no computational method capable of drawing unified inferences across modalities. We introduce MultiTME, a single, holistic generative model for jointly modeling multimodal data across multiple samples in a cohort. MultiTME addresses these challenges through a two-dimensional integration of both samples and multimodal data. MultiTME incorporates a cohort of tissue samples, enabling the joint modeling of different samples within a single analytical framework. This integration allows for a comprehensive analysis of the spatial distribution of cell types across multiple samples, enhancing the understanding of TME variability and the identification of robust biomarkers. MultiTME also integrates multimodal data with varying resolutions, such as Spatial Transcriptomics (ST), single-cell RNA sequencing (scRNA-seq), and Imaging Mass Cytometry (IMC), and provides a multi-layered understanding of the TME. This integration enables the modeling of TME at a finer scale and the discovery of cell types and subtypes that are not distinguishable using single-modal approaches. It also provides concurrent expression profiles across different modalities, leading to the identification of novel biomarkers. We demonstrate MultiTME's efficiency and scalability with a dataset of coupled ST, scRNA-seq, and IMC from a cohort of 8 lung cancer patients. MultiTME's capability to handle both intra-cohort and inter-modality variability offers a panoramic view of TME and helps identify biomarkers and therapeutic targets for cancer screening and treatment. MultiTME is open source. Citation Format: Haoran Zhang, Jeff Quinn, Liron Yoffe, Vivek Mittal, Wesley Tansey. MultiTME: A Bayesian method for integration and analysis of multimodal, multi-patient spatial profiling data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6244.

Semi-Bayesian active learning quadrature for estimating extremely low failure probabilities

The Bayesian failure probability inference (BFPI) framework provides a sound basis for developing new Bayesian active learning reliability analysis methods. However, it is still computationally challenging to make use of the posterior variance of the failure probability. This study presents a novel method called ‘semi-Bayesian active learning quadrature’ (SBALQ) for estimating extremely low failure probabilities, which builds upon the BFPI framework. The key idea lies in only leveraging the posterior mean of the failure probability to design two crucial components for active learning — the stopping criterion and learning function. In this context, a new stopping criterion is introduced through exploring the structure of the posterior mean. Besides, we also develop a numerical integration technique named ‘hyper-shell simulation’ to estimate the analytically intractable integrals inherent in the stopping criterion. Furthermore, a new learning function is derived from the stopping criterion and by maximizing it a single point can be identified in each iteration of the active learning phase. To enable multi-point selection and facilitate parallel computing, the proposed learning function is modified by incorporating an influence function. Through five numerical examples, it is demonstrated that the proposed method can assess extremely small failure probabilities with desired efficiency and accuracy.

LISA Galactic Binaries with Astrometry from Gaia DR3

Galactic compact binaries with orbital periods shorter than a few hours emit detectable gravitational waves (GWs) at low frequencies. Their GW signals can be detected with the future Laser Interferometer Space Antenna (LISA). Crucially, they may be useful in the early months of the mission operation in helping to validate LISA's performance in comparison to prelaunch expectations. We present an updated list of 55 candidate LISA-detectable binaries with measured properties, for which we derive distances based on Gaia Data Release 3 astrometry. Based on the known properties from electromagnetic observations, we predict the LISA detectability after 1, 3, 6, and 48 months using Bayesian analysis methods. We distinguish between verification and detectable binaries as being detectable after 3 and 48 months, respectively. We find 18 verification binaries and 22 detectable sources, which triples the number of known LISA binaries over the last few years. These include detached double white dwarfs, AM CVn binaries, one ultracompact X-ray binary, and two hot subdwarf binaries. We find that across this sample the GW amplitude is expected to be measured to ≈10% on average, while the inclination is expected to be determined with ≈15° precision. For detectable binaries, these average errors increase to ≈50% and ≈40°, respectively.

Investigating the hard state of MAXI J1820 + 070: a comprehensive Bayesian approach to black hole spin and accretion properties

ABSTRACT We analyse the X-ray spectrum of the black hole (BH) X-ray binary MAXI J1820 + 070 using observations from XMM-Newton and NuSTAR during ‘hard’ states of its 2018–2019 outburst. We take a fully Bayesian approach, and this is one of the first papers to present a fully Bayesian workflow for the analysis of an X-ray binary X-ray spectrum. This allows us to leverage the relatively well-understood distance and binary system properties (like inclination and BH mass), as well as information from the XMM-Newton RGS data to assess the foreground X-ray absorption. We employ a spectral model for a ‘vanilla’ disc-corona system: the disc is flat and in the plane perpendicular to the axis of the jet and the BH spin, the disc extends inwards to the innermost stable circular orbit around the BH, and the (non-thermal) hard X-ray photons are up-scattered soft X-ray photons originating from the disc thermal emission. Together, these provide tight constraints on the spectral model and, in combination with the strong prior information about the system, mean we can then constrain other parameters that are poorly understood such as the disc colour correction factor. By marginalizing over all the parameters, we calculate a posterior density for the BH spin parameter, a. Our modelling suggests a preference for low or negative spin values, although this could plausibly be reproduced by higher spins and a modest degree of disc truncation. This approach demonstrates the efficacy and some of the complexities of Bayesian methods for X-ray spectral analysis.

Tekrarlanan ölçümlerde farklı kovaryans yapılarının Bayes yöntemi ile modellenmesi

Objective: The objective of this study was to obtain solutions by modeling different covariance structures with Bayesian analysis methods in repeated measurement and to show its applicability to data in animal science. &#x0D; Materials and Methods: This article focused on the analysis of the body weight data of 4154 weaned 8-month-old lambs. Repeated measurement analyses based on the mixed effect model were evaluated with Bayesian methods. Models were created for 12 different covariance structures. As the model comparison criterion, Deviation Information Criteria based on the relationship between the fit of the data to the model and the complexity of the model were used. &#x0D; Result: Among 12 different covariance structures, the unstructured covariance structure was determined as a suitable structure for the data of this study.&#x0D; Conclusions: It was concluded that various variance-covariance structures, such as body weight, can be easily modeled in repeated measurement data. Instead of PROC MCMC methods that require complex and computational difficulties and profound coding knowledge, it was presented a relatively user-friendly and fast procedure with its theoretical structure and demonstrated its feasibility. As a result of the literature review, this is the first study in which Bayesian methods solved a wide variety of variance-covariance structure models.