Bayesian Inference Research Articles

Sequential Bayesian Phylogenetic Inference.

The ideal approach to Bayesian phylogenetic inference is to estimate all parameters of interest jointly in a single hierarchical model. However, this is often not feasible in practice due to the high computational cost. Instead, phylogenetic pipelines generally consist of sequential analyses, whereby a single point estimate from a given analysis is used as input for the next analysis (e.g., a single multiple sequence alignment is used to estimate a gene tree). In this framework, uncertainty is not propagated from step to step, which can lead to inaccurate or spuriously confident results. Here, we formally develop and test a sequential inference approach for Bayesian phylogenetic inference, which uses importance sampling to generate observations for the next step of an analysis pipeline from the posterior distribution produced in the previous step. Our sequential inference approach presented here not only accounts for uncertainty between analysis steps, but also allows for greater flexibility in software choice (and hence model availability) and can be computationally more efficient than the traditional joint inference approach when multiple models are being tested. We show that our sequential inference approach is identical in practice to the joint inference approach only if sufficient information in the data is present (a narrow posterior distribution) and/or sufficiently many importance samples are used. Conversely, we show that the common practice of using a single point estimate can be biased, e.g., a single phylogeny estimate to transform an unrooted phylogeny into a time-calibrated phylogeny. We demonstrate the theory of sequential Bayesian inference using both a toy example and an empirical case study of divergence-time estimation in insects using a relaxed clock model from transcriptome data. In the empirical example, we estimate three posterior distributions of branch lengths from the same data (DNA character matrix with a GTR+Γ+I substitution model, an amino acid data matrix with empirical substitution models, and an amino acid data matrix with the PhyloBayes CAT-GTR model). Finally, we apply three different node-calibration strategies and show that divergence-time estimates are affected by both the data source and underlying substitution process to estimate branch lengths as well as the node-calibration strategies. Thus, our new sequential Bayesian phylogenetic inference provides the opportunity to efficiently test different approaches for divergence time estimation, including branch-length estimation from other software.

Meta-analysis in Plant Pathology: Publication Patterns, Methodological Trends and Potential Future Directions.

Meta-analysis (MA) is increasingly adopted in agricultural and ecological sciences, fields at the interface with plant pathology. Our review on the use of MA in the field, since 1999, identified 79 original research papers. Fifty percent of these works were published in two APS journals, emphasizing their central role in disseminating MA research. Analysis of the number of authors per study and the authorship network revealed a tightly knit field. The network shows a few "hub" authors or groups that significantly influence research directions, with a clear geographical clustering in North and South America. Most of the MA papers focused on the effects of disease control, where fungicide was the main product. The MA investigations often focus on response variables like yield and disease severity for which the analyzed effect sizes differ, but the log of the ratio of means and untransformed means are predominant. The study selection to be included in the MA often follows systematic review standards or when clear quantitative criteria are used for study inclusion. Yet, the inclusion of data from reports, newsletters, and research trial data adds breadth. Frequentist rather than Bayesian inference is most common and SAS and R are the preferred software. Our review underscores the escalating importance of MA in plant pathology, especially for determining the mean and variability of the effects of different control methods on disease and yield. Efforts should continue to improve the detailed reporting of MA methods used, data sharing and reporting of measures of heterogeneity among studies.

A comparison of alternative ranking methods in two-stage clinical trials with multiple interventions: An application to the anxiolysis for laceration repair in children trial.

Multi-arm, multi-stage trials frequently include a standard care to which all interventions are compared. This may increase costs and hinders comparisons among the experimental arms. Furthermore, the standard care may not be evident, particularly when there is a large variation in standard practice. Thus, we aimed to develop an adaptive clinical trial that drops ineffective interventions following an interim analysis before selecting the best intervention at the final stage without requiring a standard care. We used Bayesian methods to develop a multi-arm, two-stage adaptive trial and evaluated two different methods for ranking interventions, the probability that each intervention was optimal (Pbest) and using the surface under the cumulative ranking curve (SUCRA), at both the interim and final analysis. The proposed trial design determines the maximum sample size for each intervention using the Average Length Criteria. The interim analysis takes place at approximately half the pre-specified maximum sample size and aims to drop interventions for futility if either Pbest or the SUCRA is below a pre-specified threshold. The final analysis compares all remaining interventions at the maximum sample size to conclude superiority based on either Pbest or the SUCRA. The two ranking methods were compared across 12 scenarios that vary the number of interventions and the assumed differences between the interventions. The thresholds for futility and superiority were chosen to control type 1 error, and then the predictive power and expected sample size were evaluated across scenarios. A trial comparing three interventions that aim to reduce anxiety for children undergoing a laceration repair in the emergency department was then designed, known as the Anxiolysis for Laceration Repair in Children Trial (ALICE) trial. As the number of interventions increases, the SUCRA results in a higher predictive power compared with Pbest. Using Pbest results in a lower expected sample size when there is an effective intervention. Using the Average Length Criterion, the ALICE trial has a maximum sample size for each arm of 100 patients. This sample size results in a 86% and 85% predictive power using Pbest and the SUCRA, respectively. Thus, we chose Pbest as the ranking method for the ALICE trial. Bayesian ranking methods can be used in multi-arm, multi-stage trials with no clear control intervention. When more interventions are included, the SUCRA results in a higher power than Pbest. Future work should consider whether other ranking methods may also be relevant for clinical trial design.

Improving Microstructural Estimation in Time-Dependent Diffusion MRI With a Bayesian Method.

Accurately fitting diffusion-time-dependent diffusion MRI (td-dMRI) models poses challenges due to complex and nonlinear formulas, signal noise, and limited clinical data acquisition. Introduce a Bayesian methodology to refine microstructural fitting within the IMPULSED (Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion) model and optimize the prior distribution within the Bayesian framework. Retrospective. Involving 69 pediatric patients (median age 6 years, interquartile range [IQR] 3-9 years, 61% male) with 41 low-grade and 28 high-grade gliomas, of which 76.8% were identified within the brainstem or cerebellum. 3 T, oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE). The Bayesian method's performance in fitting cell diameter ( ), intracellular volume fraction ( ), and extracellular diffusion coefficient ( ) was compared against the NLLS method, considering simulated and experimental data. The tumor region-of-interest (ROI) were manually delineated on the b0 images. The diagnostic performance in distinguishing high- and low-grade gliomas was assessed, and fitting accuracy was validated against H&E-stained pathology. T-test, receiver operating curve (ROC), area under the curve (AUC) and DeLong's test were conducted. Significance considered at P < 0.05. Bayesian methodology manifested increased accuracy with robust estimates in simulation (RMSE decreased by 29.6%, 40.9%, 13.6%, and STD decreased by 29.2%, 43.5%, and 24.0%, respectively for , , and compared to NLLS), indicating fewer outliers and reduced error. Diagnostic performance for tumor grade was similar in both methods, however, Bayesian method generated smoother microstructural maps (outliers ratio decreased by 45.3% ± 19.4%) and a marginal enhancement in correlation with H&E staining result (r = 0.721 for compared to r = 0.698 using NLLS, P = 0.5764). The proposed Bayesian method substantially enhances the accuracy and robustness of IMPULSED model estimation, suggesting its potential clinical utility in characterizing cellular microstructure. 3 TECHNICAL EFFICACY: Stage 1.

Evaluating human landing catches as a measure of mosquito biting and the importance of considering additional modes of action

Entomological evaluations of vector control tools often use human landing catches (HLCs) as a standard measure of a direct human-vector contact. However, some tools have additional characteristics, such as mortality, and HLCS are not sensitive for measuring other effects beyond landing inhibition. Therefore, additional measures may need to be considered when evaluating these tools for public health use. This study has two main aims (1) the evaluate the accuracy of HLCs as a proxy for feeding and (2) to compare the predicted reduction in vectorial capacity when we do and do not consider these additional characteristics. To achieve this, we analyse previously published semi-field data from an experiment which used HLCs and another where mosquitoes were allowed to feed in the presence of different dosages of the volatile pyrethroid spatial repellent, transfluthrin. We compare results for two mathematical models: one which only considers the reduction in feeding effect and one which also considers mortality before and after feeding (using data gathered by the aspiration of mosquitoes after the semi-field feeding/landing period and 24 h survival monitoring). These Bayesian hierarchical models are parameterised using Bayesian inference. We observe that, for susceptible mosquitoes, reduction in landing is underestimated by HLCs. For knockdown resistant mosquitoes the relationship is less clear; with HLCs sometimes appearing to overestimate this characteristic. We find HLCs tend to under-predict the relative reduction in vectorial capacity in susceptible mosquitoes while over-predicting this impact in knockdown-resistant mosquitoes. Models without secondary effects have lower predicted relative reductions in vectorial capacities. Overall, this study highlights the importance of considering additional characteristics to reduction in biting of volatile pyrethroid spatial repellents. We recommend that these are considered when evaluating novel vector control tools.

Meta-Analysis of 5-Fraction Preoperative Radiotherapy for Soft Tissue Sarcoma.

Studies investigating preoperative 5-fraction radiation therapy (RT) for soft tissue sarcoma (STS) are limited. We performed a meta-analysis to determine the efficacy and safety of this treatment paradigm. This study-level meta-analysis was conducted using Bayesian methods. Statistical estimation for risk of outcome rates was conducted by posterior mean and 95% highest posterior density (HPD) intervals. Studies with 2-year local control (LC) and description of major wound complications (MWC) per the CAN-NCIC-SR2 study were included and served as the primary endpoints. Secondary endpoints included rates of acute and late toxicity. A total of 10 studies were identified and 7 met the inclusion criteria. Subgroup analyses were performed for ≥30 Gy vs <30 Gy. A total of 209 patients from 7 studies were included. Five studies used ≥30 Gy (n=144), and 2 studies <30 Gy (n=64). Median follow-up was 29 months (range: 21 to 57mo). Primary tumor location was lower extremity in 68% and upper extremity in 22%. Most tumors were intermediate or high grade (95%, 160/169), and 50% (79/158) were >10cm. The two-year LC for the entire cohort was 96.9%, and the rate of MWC was 30.6%. There was a trend toward improved LC with ≥ 30 Gy (95% HPD: 0.95 to 0.99 vs 0.84 to 0.99). There was no difference in MWC (95% HPD: 0.18 to 0.42 vs 0.17 to 0.55) or late toxicity between the groups. Preoperative 5-fraction RT for STS demonstrates excellent 2-year LC with MWC and toxicity similar to standard fractionation preoperative RT. Multi-institutional trials with a universal RT protocol are warranted.

Bayesian Meta-Analysis of Health State Utility Values: A Tutorial with a Practical Application in Heart Failure.

Researchers incorporate health state utility values as inputs to inform economic models. However, for a particular health state or condition, multiple utility values derived from different studies typically exist and a single study is often insufficient to represent the best available source of utility needed to inform policy decisions. The purpose of this paper is to provide an introductory guidance for conducting Bayesian meta-analysis of health state utility values to generate a single parameter input for economic evaluation, using R. The tutorial is illustrated using data from a systematic review of health state utilities of patients with heart failure, with 21 studies that reported utilities measured using the EuroQol-5D (EQ-5D). Explanations, key considerations and suggested readings are provided for each step of the tutorial, adhering to a clear workflow for conducting Bayesian meta-analysis: (1) setting-up the data; (2) employing methods to impute missing standard deviations; (3) defining the priors; (4) fitting the model; (5) diagnosing model convergence; (6) interpreting the results; and (7) performing sensitivity analyses. The posterior distributions for the pooled effect size (i.e. mean health state utility) and between-study heterogeneity are discussed and interpreted in light of the data, priors and models used. We hope that this tutorial will foster interest in Bayesian methods and their applications in the meta-analysis of utilities.

A novel method of fuel consumption prediction for wing-diesel hybrid ships based on high-dimensional feature selection and improved blending ensemble learning method

Establishing an accurate fuel consumption prediction model is essential to optimize energy efficiency in wing-diesel hybrid ships. This study proposes an improved Blending ensemble learning method for predicting fuel consumption. Initially, the original data is cleaned using artificial experience and Interquartile Range (IQR) method. A high-dimensional feature selection method, variance analysis- Pearson correlation-mutual information-permutation importance (VA-PC-MI-PI), is then proposed to identify 16 key features from the initial 111 features. Furthermore, 9 algorithms are employed for prediction analysis under different temporal resolution data conditions. Based on the results, optimal time step and algorithm models are determined to construct the Blending ensemble model (BEM). Subsequently, the feature data of “sailing speed” and “bow draft”, which account for the highest permutation importance, are imported into the metadata set to alleviate insufficient BEM data utilization. Additionally, Extra Trees (ET) model is chosen as the meta-learner for adaptation and Bayesian method is employed for global optimization. The resulting improved Blending ensemble model (IBEM) exhibits optimal prediction accuracy and generalization ability with an RMSE of 66.04 kg/h, representing a reduction of 16.99% and 13.86% compared to the Support Vector Machine (SVM) model and BEM, respectively. Additionally, the IBEM exhibits significantly smaller residuals and absolute residuals, demonstrating superior predictive stability and consistency. This study provides an important reference and basis for energy efficiency prediction, control, and optimization of wing-diesel hybrid ships.

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

ALK has been to be involved in the uptake and regulation of 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. This study investigated the relationship between ALK inhibitors and seizures through a disproportionality analysis using the FAERS. The target drugs were the ALK inhibitors crizotinib, ceritinib, alectinib, brigatinib, and lorlatinib. The seizures covered were defined HLGT: ''Seizures (incl subtype)'' including HLT: ''Seizures and seizure disorders NEC.'' This study used the IC, a signal score, as a Bayesian statistical method for disproportionality analysis. The signal scores of ''Seizures and seizure disorders 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. 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.

Study on the remaining useful life prediction method of hydraulic slide valve based on degradation-competition failure sharing random effect identification

Purpose Hydraulic slide valve failure often results from competing failure modes, termed competitive failure. To enhance prediction accuracy for hydraulic slide valve remaining useful life, the authors propose a method incorporating competitive failure and Monte Carlo simulation. This method allows for more accurate prediction of hydraulic slide valve remaining useful life. Design/methodology/approach In this paper, the competitive failure mode of the hydraulic slide valve is analyzed by studying the two failure modes of the hydraulic slide valve, and the prediction of the remaining useful life of the hydraulic slide valve is studied by using the sample set generated by Monte Carlo simulation and the competitive failure joint model. Findings The results show that the proposed prediction method based on competitive failure and Monte Carlo simulation is more accurate than the traditional Bayesian joint model prediction method when dealing with the failure mode competition phenomenon of hydraulic slide valve. Originality/value In this paper, the remaining useful life prediction of hydraulic slide valve with competitive failure characteristics is studied, which provides a new idea for the remaining useful life prediction method. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2023-0361/

Rectifying a century of misidentifications: a taxonomic re-evaluation of sipo snakes (Colubridae: Chironius) on Trinidad

The island of Trinidad has several endemic reptile species that in some cases are morphologically indistinguishable, or almost so, from their mainland counterparts. In particular, many snakes from the island have not been examined thoroughly with modern scientific methods and may therefore be misidentified. At least two species of sipo snakes (Chironius carinatus, C. septentrionalis) are reported to inhabit Trinidad, both being considered species already inhabiting the mainland, though their identities are based solely on morphology. Here, we evaluate the molecular distinctiveness of these Trinidadian snakes and assess their relationships to other members of the genus. We constructed a multi-locus data set (12S rRNA, 16S rRNA, cyt b, ND4, cmos, NT3, Rag-1, Rag-2) including novel sequences and those available on GenBank to perform Bayesian and maximum likelihood phylogenetic analyses. Our phylogenetic reconstruction elucidates the identity of both Trinidad species, which have been misidentified since their discovery, and we provide a literature review of their taxonomic history. More specifically, our results suggest that the Trinidad population of Chironius carinatus is an undescribed species, that we describe herein as Chironius nigelnoriegai sp. nov., and is the sister to C. flavopictus. Our results also demonstrate that the Trinidad population previously identified as C. septentrionalis is instead the mainland species C. cochranae, a member of a clade with C. foveatus, C. laurenti, and C. multiventris. Finally, we generated a time tree and inferred that our new species separated from C. flavopictus approximately 4 million years ago in the Pliocene, a time when the island of Trinidad detached from Northern Venezuela. This work contributes to a better understanding of species diversity of Trinidad and we hope that it assists in conservation efforts towards this important endemic region. These findings support the prospect of rapid speciation on Trinidad and implies that more extensive surveys of island squamates will reveal additional cryptic diversity. http://zoobank.org/urn:lsid:zoobank.org:pub:500DB91B-62CD-4051-9831-0BEEAC64C8E2

Optimal parameter estimation for uncertain structural systems under unknown random excitations

An optimal parameter estimation method for uncertain structural systems under unknown random excitations is proposed, which combines the Bayesian inference and stochastic dynamics using geometrically averaged likelihood and optimal response estimation. The general description of optimal parameter estimation problems for uncertain dynamic systems under only stochastic responses measured is presented. The posterior probability density conditional on measured responses is expressed by the likelihood function conditional on system parameters based on Bayes’ theorem. For finite time processes, the parameter estimation problem as the probability integral of conditional means is converted into the optimization problem expressed as maximizing the posterior probability density. A geometrically averaged likelihood function is defined and used for calculating the logarithmic posterior probability density. This estimation can avoid the numerical singularity of the likelihood function and reduce the effects of incomplete posterior probability density and inaccurate prior statistics of unknown random excitations, and then it will be more reasonable and effective. Furthermore, the differential equations for system response means and covariances are derived and solved based on stochastic dynamics theory. The means and covariances conditional on responses at the present instant are expressed by the previous statistics based on optimal response estimation. By combining two results, the analytical expressions of the averaged likelihood function and logarithmic posterior probability density are obtained which will be more reliable and accurate. The proposed optimal estimation method is verified by numerical results for a five-storey frame structure under base random excitation. The estimated results are not affected by the prior statistics errors of random excitation as a factor. For noisy observation, the Kalman filtering is incorporated in the estimation method and hence the estimation is more accurate and robust even for low signal noise ratios. The optimal estimation method has the potential for application to general uncertain systems.

Probabilistic deep learning approach for fatigue crack width estimation and prognosis in lap joint using acoustic waves

Abstract Accurate fatigue crack width estimation is crucial for aircraft safety, however, limited research exists on (i) the direct relationship between fatigue crack width and Lamb wave signatures and (ii) probabilistic artificial intelligence (AI) approach for automated analysis using acoustic emission waveforms. This paper presents a probabilistic deep learning approach for fatigue crack width estimation, employing an automated wavelet feature extractor and probabilistic Bayesian neural network. A dataset constituting the fatigue experiment on aluminum lap-joint specimens is considered, in which Lamb wave signals were recorded at several time instants for each specimen. Signals acquired from the piezo actuator-receiver sensor pairs are related to the optically measured surface crack length. The sensitive features are automatically extracted from the signals using decomposition techniques called maximal overlap discrete wavelet transform (MODWT). The extracted features are then mapped through the deep learning model, which incorporates Bayesian inference to account for both aleatoric as well as epistemic uncertainty, that provides outcomes in the form of providing probabilistic estimates of crack width with uncertainty quantification. Thus, employing an automated wavelet feature extractor (MODWT) on a dataset of fatigue experiments, the framework learns the relationship between Lamb wave signals and crack width. Validation on unseen in-situ data demonstrates the efficacy of the approach for practical implementation, paving the way for more reliable fatigue life prognosis.

Telework and the day-to-day variability of travel behaviour: The specificities of Fridays

This study investigates the uniqueness of Fridays for telework in relation to commuting and non-work travel patterns. Utilizing Bayesian analysis on UK National Travel Survey data (2015–2019), it explores differences in trip frequency, distance, time, and CO2 emissions across days, particularly focusing on teleworkers versus other workers. The findings highlight Fridays as a distinct day for teleworkers, marked by a 20% reduction in commuting and increased non-work trips, particularly during afternoons and evenings, compared to other weekdays and non-teleworkers. Key factors influencing these patterns include commuting mode, particularly private car use, and rural residency. The study concludes by considering telework’s potential in reducing travelrelated CO2 emissions and its impact on public transport and local economies, especially post-COVID-19

First report of leaf spot disease caused by Pseudopithomyces chartarum on Lonicera caerulea L. in Heilongjiang Province, China.

Blue honeysuckle (Lonicera caerulea L.) cultivation has gradually expanded in China but continues to be limited by challenges such as leaf spot disease. Between September 2022 and September 2023, a leaf spot disease was observed on approximately 30% of 'Lanjingling' blue honeysuckles grown in a 2.66 ha field (a total of about 11,000 plants) in Jiamusi city (130.47°E, 46.16°N), Heilongjiang Province, China. Affected plants displayed brown necrotic lesions on their leaves that gradually expanded in area until the leaves fell off the plant entirely. Small, 3 to 4 mm segments of infected tissue from 50 randomly selected leaves were surface sterilized with 75% ethanol for 30 s and 5% sodium hypochlorite (NaOCl) for 3 min, rinsed three times with sterile distilled water, dried on paper towels, and plated in 9 cm Petri dishes containing potato dextrose agar (PDA) (Yan et al. 2022). Five pathogens (LD-232, LD-233, LD-234, LD-235, and LD-236) were isolated on PDA and displayed a conidia morphology consistent with Pseudopithomyces spp. (Perelló et al. 2017). The fungal colonies on PDA were villiform, white, and whorled and had sparse aerial mycelium on the surface with black conidiomata. The conidia were obpyriform and dark brown, had 0 to 3 transverse and 0 to 1 longitudinal septa, and measured 9.00 to 15.30 μm × 5.70 to 9.30 μm in size (n = 50). Genomic DNA was extracted from a representative isolate, LD-232, for molecular verification and PCR amplification was performed with ITS1/ITS4 (White et al. 1990), LROR/LR7 (Carbone and Kohn 1999), and RPB2-5F2/RPB2-7CR (Liu et al. 1999) primers. Sequences of LD-232 ITS (OR835654), LSU (OR835652), and RPB2 (OR859769) revealed 99.8% (530/531 nt), 98.8% (639/647 nt), and 99.8% (1015/1017 nt) shared identity with Pseudopithomyces chartarum sequences (OP269600, OP237014, and MK434892), respectively (Wu et al. 2023). Bayesian inference (BI) was used to construct the phylogenies using Mr. Bayes v. 3.2.7 to confirm the identity of the isolates (Ariyawansa et al. 2015). Phylogenetic trees cannot be constructed based on the genes' concatenated sequences because selective strains do not have complete rDNA-ITS, LSU, and RPB2 sequences. Therefore, based on the morphological characteristics and molecular phylogeny, LD-232 was identified as P. chartarum (Perelló et al. 2017; Wu et al. 2023). A pathogenicity test was performed with six healthy, two-year-old 'Lanjingling' blue honeysuckle plants. Three plants were inoculated by spraying the LD-232 conidial suspension (1 × 106 spores/ml) or clean water as an experimental control condition (Wu et al. 2023; Yan et al. 2023). All plants were cultured in a greenhouse at 28℃ under a 12-h light/dark cycle, and each experiment was replicated three times. Typical leaf spot symptoms were observed on inoculated leaves after 10 days. The same pathogens were reisolated from infected leaves, displayed the same morphological and molecular traits, and were again identified as P. chartarum, confirming Koch's postulate. P. chartarum previously caused leaf spot disease on Tetrapanax papyrifer in China (Wu et al. 2023). To our knowledge, this is the first report of blue honeysuckle leaf spot caused by P. chartarum in China. Identification of P. chartarum as a disease agent on blue honeysuckle will help guide future management of leaf diseases for this economically important small fruit tree.

Correcting an Off-Nadir to a Nadir Land Surface Temperature Using a Multitemporal Thermal Infrared Kernel-Driven Model during Daytime

Land surface temperature (LST) is a fundamental parameter in global climate, environmental, and geophysical studies. Remote sensing is an essential approach for obtaining large-scale and frequently updated LST data. However, due to the wide field of view of remote sensing sensors, the observed LST with diverse view geometries suffers from inconsistency caused by the thermal radiation directionality (TRD) effect, which results in LST products being incomparable, especially during daytime. To address this issue and correct current off-nadir LSTs to nadir LSTs, a semi-physical time-evolved kernel-driven model (TEKDM) is proposed, which depicts multitemporal TRD patterns during the daytime. In addition, we employ a Bayesian optimization method to calibrate seven unknown parameters in the TEKDM. Validation results using the U.S. Climate Reference Network (USCRN) sites show that the RMSE (MBE) for GOES-16 and MODIS off-nadir LST products is reduced from 3.29 K (−2.0 K) to 2.34 K (−0.02 K), with an RMSE reduction of 0.95 K (29%) and a significant reduction in systematic bias. Moreover, the proposed method successfully eliminates the angular and temporal dependence of the LST difference between the satellite off-nadir LST and in situ nadir LST. In summary, this study presents a feasible approach for estimating the high-accuracy nadir LST, which can enhance the applicability of LST products in various domains.

Bayesian Maximal Reliability Evaluation Using Latent Variable Modeling

ABSTRACT We discuss an application of Bayesian factor analysis for estimation of the optimal linear combination and associated maximal reliability of a multi-component measuring instrument. The described procedure yields point and credibility interval estimates of this reliability coefficient, which are readily obtained in educational and behavioral measurement research. In addition, the outlined method permits evaluation of the gain in measurement consistency resulting from utilizing the maximal reliability coefficient instead of the traditionally used overall sum score reliability. The discussed Bayesian inference approach is applicable with widely available software in empirical studies, and is illustrated using a data example.

Bayesian inference for the onset time and epidemiological characteristics of emerging infectious diseases

BackgroundEmerging infectious diseases pose a significant threat to global public health. Timely detection and response are crucial in mitigating the spread of such epidemics. Inferring the onset time and epidemiological characteristics is vital for accelerating early interventions, but accurately predicting these parameters in the early stages remains challenging.MethodsWe introduce a Bayesian inference method to fit epidemic models to time series data based on state-space modeling, employing a stochastic Susceptible-Exposed-Infectious-Removed (SEIR) model for transmission dynamics analysis. Our approach uses the particle Markov chain Monte Carlo (PMCMC) method to estimate key epidemiological parameters, including the onset time, the transmission rate, and the recovery rate. The PMCMC algorithm integrates the advantageous aspects of both MCMC and particle filtering methodologies to yield a computationally feasible and effective means of approximating the likelihood function, especially when it is computationally intractable.ResultsTo validate the proposed method, we conduct case studies on COVID-19 outbreaks in Wuhan, Shanghai and Nanjing, China, respectively. Using early-stage case reports, the PMCMC algorithm accurately predicted the onset time, key epidemiological parameters, and the basic reproduction number. These findings are consistent with empirical studies and the literature.ConclusionThis study presents a robust Bayesian inference method for the timely investigation of emerging infectious diseases. By accurately estimating the onset time and essential epidemiological parameters, our approach is versatile and efficient, extending its utility beyond COVID-19.

Bayesian Inference Based on Monte Carlo Technique for Multiplier of Performance Shaping Factor

Abstract The Human Error Probabilities (HEP) can be estimated using multipliers that correspond to the level of Performance Shaping Factors (PSFs) in the Human Reliability Analysis (HRA). This paper focuses on the adjustment of multipliers through Bayesian inference based on Monte Carlo techniques using the experimental results from simulators. Markov Chain Monte Carlo (MCMC) and Bayesian Monte Carlo (BMC) are used as Bayesian inference methods based on Monte Carlo techniques. MCMC is utilized to obtain the posterior distribution of the multipliers. BMC is used for the estimation of the moments of the posterior distribution such as the mean and variance. The results obtained by MCMC and that by BMC well agree with the reference results. As a case study, the data assimilation was performed using the results of the simulator experiment of Halden reactor. The results show that the multiplier changes by the result of a particular scenario and HEP of another scenario that uses the same multiplier also changes by data assimilation. Also, in the case study, the correlation between multipliers is obtained by the data assimilation and the correlation contributes to the reduction of uncertainty of HEP.

Bayesian issues in the 1950s: an episode involving Karl Popper and Jimmie Savage

Abstract In the 1950s, the growing importance of Bayesian inference attracted both supporters and critics. In 1958, the philosopher Karl Popper published what he called a paradox that purported to show the foolishness of subjectivist Bayesian inference. In correspondence, Jimmie Savage pointed out what he considered an error in his reasoning, but Popper was unmoved and did not change his example when he reprinted it. It is argued that Popper did not acknowledge that the new subjectivists reasoned with distributions, not simply expected values. Excerpts from Jimmie Savage’s correspondence at the time with Popper and with Allen Wallis highlight the issues involved at this time when Bayesian statisticians were seeking general acceptance.