Posterior Distribution Research Articles

Epidemiology of acute lymphoblastic leukaemia in Sardinia, Italy: Age, sex, and environmental correlates

Using a database of 1974–2003 incident cases of haematological malignancies, we explored the time trend, geographic spread and socio-economic and environmental correlates of ALL incidence in Sardinia, Italy, by sex and age. The age- and sex-standardized (World population) ALL incidence rate was 2.0 per 100,000 (95% CI 1.8 – 2.1) and showed variable trend patterns by sex and age. In the total population, ALL incidence showed an annual per cent change of −1.4% (95% CI -0.59 – -3.34) over the study period, with a knot separating a downward slope in 1974–1996 from an increase in 1996–2003. ALL incidence replicated such pattern in women but not men, whose incidence did not substantially vary over the study period (APC = −2.57%, 95% CI -5.45 – 0.26). Among women, the spatial analysis suggested a clustering of ALL in the southwestern part of the region, whilst only a commune had a high posterior probability of a high ALL incidence among men. Three unrelated communes showed a high posterior probability of ALL at age ≤ 24; only the most populated urban centre showed excess cases at age ≥ 25 years. There was no correlation between the geographic spread of ALL at ages ≤ 24 and ≥ 25 years (p = 0.082). Urban residence was a risk factor for the younger age group. Residences near industrial settlements and in the most populated urban centre were risk factors for subjects aged ≥ 25 years. Our findings suggest age-related differences in ALL aetiology.

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.

Genomic epidemiology of Staphylococcus aureus from the Iberian Peninsula highlights the expansion of livestock associated-CC398 towards wildlife

Staphylococcus aureus is a versatile pathobiont, exhibiting a broad host range, including humans, other mammals, and avian species. Host specificity determinants, virulence, and antimicrobial resistance genes are often shared by strains circulating at the animal-human interface. While transmission dynamics studies have shown strain exchange between humans and livestock, knowledge of the source, genetic diversification, and transmission drivers of S. aureus in wildlife lag behind.In this work, we explore a wide array of S. aureus genomes from different sources in the Iberian Peninsula to understand population structure, gene content and niche adaptation at the human-livestock-wildlife nexus. Through Bayesian inference, we address the hypothesis that S. aureus strains in wildlife originate from humanized landscapes, either from contact with humans or through interactions with livestock. Phylogenetic reconstruction applied to whole genome sequence data was completed with a dataset of 450 isolates featuring multiple clones from the 1990–2022 period and a subset of CC398 strains representing the 2008–2022 period. Phylodynamic signatures of S. aureus from the Iberian Peninsula suggest widespread circulation of most clones among humans before jumping to other hosts. The number of transitions of CC398 strains within each host category (human, livestock, wildlife) was high (88.26 %), while the posterior probability of transitions from livestock to wildlife was remarkably high (0.99). Microbial genome-wide association analysis did not evidence genome rearrangements nor biomarkers suggesting S. aureus niche adaptation to wildlife, thus supporting recent spill overs. Altogether, our findings indicate that S. aureus isolates collected in the past years from wildlife most likely represent multiple introduction events from livestock. The clonal origin of CC398 and its potential to disseminate and evolve through different animal host species are highlighted, calling for management practices at the livestock-wildlife axis to improve biosecurity and thus restrict S. aureus transmission and niche expansion along gradients of human influence.

Classification performance of the Sella-Tunis et al. (2017) sex estimation method in Czech population: different posterior probability threshold approaches.

In this study we tested classification performance of a sex estimation method from the mandible originally developed by Sella-Tunis et al. (2017) on a heterogeneous Israeli population. Mandibular linear dimensions were measured on 60 CT scans derived from the Czech living population. Classification performance of Israeli discriminant functions (DFs-IL) was analyzed in comparison with calculated Czech discriminant functions (DFs-CZ) while different posterior probability thresholds (currently discussed in the forensic literature) were employed. Our results comprehensively illustrate sensitivity of different discriminant functions to population differences in body size and degree of sexual dimorphism. We demonstrate that the error rate may be biased when presented per posterior probability threshold. DF-IL 1 showed least sensitivity to population origin and fulfilled criteria of sufficient classification performance when applied on the Czech sample with a minimum posterior probability threshold of 0.88 reaching overall accuracy ≥ 95%, zero sex bias, and 80% of classified individuals. The last parameter was higher in DF-CZ 1 which was the main difference between those two DFs suggesting relatively low dependance on population origin. As the use of population-specific methods is often prevented by complicated assessment of population origin, DF-IL 1 is a candidate for a sufficiently robust method that could be reliably applied outside the reference sample, and thus, its classification performance deserves further testing on more population samples.

Task-Dependent Brain Activity in Generalized Anxiety Disorder Determined By Bayesian Spatio-Temporal Single Case Model

Introduction: Data obtained from functional magnetic resonance imaging (fMRI) have a complex structure. Considering the special features of this type of data in analyses is of particular importance. Previous studies on generalized anxiety disorder (GAD) as a prevalent mental disorder using functional neuroimaging have had conflicting results. In this study, we apply a Bayesian spatiotemporal model to this type of data which considers both spatial and temporal dependence among regions which is one of the most essential features to consider. Methods: In this single-subject study, we analyze data from a patient with GAD and a healthy participant. Both participants are 24-year-old women who are assigned an emotion reactivity task (matching neutral and negative facial expressions) inside a scanner. The spatial Bayesian variable selection method is used to detect blood oxygen level-dependent activation in fMRI data. Results: Activation areas in neutral and negative facial expressions are provided for both participants by posterior probability map. The results of our study show a greater level of activity in the GAD participant in comparison to the healthy participant in responding to the negative matching task. Conclusion: the GAD patient showed more neural activity in response to negative facial expressions than the healthy participant in brain regions related to emotional response in the areas of the frontal Pole, middle frontal gyrus, insular cortex, and frontal orbital cortex. Moreover, the inferior frontal gyrus in the patient with GAD showed more reaction to negative emotional stimuli.

Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks

With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated. UQ in material property prediction poses unique challenges, including multi-scale and multi-physics nature of materials, intricate interactions between numerous factors, limited availability of large curated datasets, etc. In this work, we introduce a physics-informed Bayesian Neural Networks (BNNs) approach for UQ, which integrates knowledge from governing laws in materials to guide the models toward physically consistent predictions. To evaluate the approach, we present case studies for predicting the creep rupture life of steel alloys. Experimental validation with three datasets of creep tests demonstrates that this method produces point predictions and uncertainty estimations that are competitive or exceed the performance of conventional UQ methods such as Gaussian Process Regression. Additionally, we evaluate the suitability of employing UQ in an active learning scenario and report competitive performance. The most promising framework for creep life prediction is BNNs based on Markov Chain Monte Carlo approximation of the posterior distribution of network parameters, as it provided more reliable results in comparison to BNNs based on variational inference approximation or related NNs with probabilistic outputs.

Cyclic behavior of laminated bio-based connections with slotted-in steel plates: Genetic algorithm, deterministic neural network-based model parameter identification, and uncertainty quantification

To support more sustainable construction, this paper experimentally investigates the cyclic behavior of laminated timber (Laminated Veneer Lumber (LVL)) and glubam (Glue Laminated Bamboo) connections with slotted-in steel plates in terms of experimental test, numerical simulations and parameter identification. Experimental tests included eight different configurations: two materials (LVL and glubam), two bolt diameters (8 and 10 mm), and one or two bolts. Two different cyclic-loading protocols were applied for each type of connection: only tension and tension/compression. The observed behavior is then compared to a finite element model developed in OpenSeesPy, which takes into account factors such as sliding, contact, pinching, cyclic stiffness, and strength degradation. To identify the best set of parameters for the model, three different approaches are considered: genetic algorithm, fast deterministic neural network, and probabilistic Bayesian method. First, the model identification is carried out by means of a genetic algorithm-based optimization. The parameter-identification results are evaluated in terms of elastic stiffness, yielding point, and ductility. Next, a sensitivity analysis is performed to determine the significance of the parameters, and an innovative approach combining neural network and sensitivity analysis is proposed for fast and preliminary parameter identification. Then, probabilistic Bayesian identification is employed to calculate the posterior distribution of the model parameters identified and the confidence bounds of the estimated response. Finally, different model identification parameters are compared and suggestions for algorithm selection are provided.

Unveiling potential therapeutic targets for diabetes-induced frozen shoulder through Mendelian randomization analysis of the human plasma proteome

IntroductionThis study aimed to assess the causal relationship between diabetes and frozen shoulder by investigating the target proteins associated with diabetes and frozen shoulder in the human plasma proteome through...

First report of leaf spot on Daphniphyllum macropodum caused by Neopestalotiopsis clavispora in China.

Daphniphyllum macropodum Miq., an evergreen arbor, is widely cultivated in southern China for its ornamental and medicinal value (Su et al. 2013). In October 2019, a severe leaf spot was observed on D. macropodum in Jinggangshan National Nature Reserve in Ji'an city, Jiangxi, China (114°06'23″E, 26°32'25″N). The plants were about 15 years old, and the disease incidence was estimated to be 15% (4/26 plants). The disease primarily appeared as small black spots on the leaves. At the late stage, the spots enlarged and coalesced into regular or irregular gray necrotic lesions with dark margins. We collected five samples per plant and total 20 samples were collected to isolated the pathogen strains. The margin of the diseased tissues was cut into 5 mm × 5 mm pieces; surface disinfected with 70% ethanol and 2% NaOCl for 30 s and 60 s, respectively; and rinsed thrice with sterile water. Tissues were placed on potato dextrose agar (PDA) and incubated at 25°C in the dark. Pure cultures were obtained by single-spore isolation method, and the representative isolates, JRM3, JRM6, and JRM8 were used for morphological studies and phylogenetic analyses. The colonies of three isolates grown on PDA were white, cottony, and flocculent, contained undulate edges with dense aerial mycelium on the surface at 25 °C. Conidiomata was black conidial masses on PDA. Conidia were 5-celled, clavate to fusiform, smooth, 19.3 to 24.4 long × 6.1 to 8.6 μm wide (n = 50). The 3 median cells were dark brown to olivaceous, the central cell was darker than the other 2 cells, and the basal and apical cells were hyaline. All conidia developed one basal appendage (3.4 to 8.3 μm long; n = 50), and 2 to 3 apical appendages (18 to 32 μm long; n = 50), filiform. The morphological characteristics of the isolates are comparable with those of the genus Neopestalotiopsis (Maharachchikumbura et al. 2014). The internal transcribed spacer (ITS) regions, β-tubulin 2 (TUB2) and translation elongation factor 1-alpha (TEF1-α) were amplified from genomic DNA for the three isolates using primers ITS1/ITS4, T1/Bt-2b, EF1-728F/EF-2 (Maharachchikumbura et al. 2014), respectively. The sequences of the isolates were submitted to GenBank (ITS, OQ372202 to OQ372204; TUB2, OQ390129 to OQ390131; TEF1-α, OQ390126 to OQ390128). A maximum likelihood and Bayesian posterior probability analyses using IQtree v. 1.6.8 and Mr. Bayes v. 3.2.6 with the concatenated sequences placed JRM3, JRM6, and JRM8 in the clade of N. clavispora. Based on the multi-locus phylogeny and morphology, three isolates were identified as N. clavispora. To confirm pathogenicity, eight healthy 10-year-old D. macropodum plants growing in the field were chosen, and 4 leaves per plant were wounded with a sterile needle and inoculated with 10 μL conidial suspension per leaf (106 conidia/ml). Eight plants inoculated with sterile water were used as control. All the inoculated leaves were covered with plastic bags to maintian a humidity environment for 2 days. The leaves inoculated with conidial suspension showed similar symptoms to those observed in the field, whereas control leaves were asymptomatic for 10 days. The same fungus were re-isolated from the lesions, whereas no fungus was isolated from control leaves. N. clavispora was determined as the pathogen of a variety of plant diseases, including Kadsura coccinea (Xie et al. 2018), Dendrobium officinale (Cao et al. 2022), Macadamia integrifolia (Santos et al. 2019). However, this is the first report of N. clavispora infecting D. macropodum in China. This work provided crucial information for epidemiologic studies and appropriate control strategies for this newly emerging disease.

A bi-endpoint expectation-maximisation algorithm for re-estimating sample size for the time-to-event endpoint under the blind condition

Abstract The expectation-maximisation (EM) algorithm can be used to adjust the sample size for the time-to-event endpoint without unblinding. Nevertheless, censoring or unreliable initial estimates may render inconsistent estimates by the EM algorithm. To address these limitations, we propose a bi-endpoint EM algorithm that incorporates the time-to-event endpoint and another endpoint, which can encompass various endpoint types and is not limited to efficacy indicators, during the EM iterations. Additionally, we suggest 2 approaches for choosing initial estimates. The application conditions are as follows: (i) at least one endpoint’s initial estimate is reliable and (ii) the influence of this endpoint on the posterior distribution of the latent variable exceeds that of another endpoint.

Uncertainty Quantification of Process- Property-Structure Linkage for Fused Filament Fabrication Parts

Uncertainty Quantification of Process- Property-Structure Linkage for Fused Filament Fabrication Parts

A deep learning method for comparing Bayesian hierarchical models.

Bayesian model comparison (BMC) offers a principled approach to assessing the relative merits of competing computational models and propagating uncertainty into model selection decisions. However, BMC is often intractable for the popular class of hierarchical models due to their high-dimensional nested parameter structure. To address this intractability, we propose a deep learning method for performing BMC on any set of hierarchical models which can be instantiated as probabilistic programs. Since our method enables amortized inference, it allows efficient re-estimation of posterior model probabilities and fast performance validation prior to any real-data application. In a series of extensive validation studies, we benchmark the performance of our method against the state-of-the-art bridge sampling method and demonstrate excellent amortized inference across all BMC settings. We then showcase our method by comparing four hierarchical evidence accumulation models that have previously been deemed intractable for BMC due to partly implicit likelihoods. Additionally, we demonstrate how transfer learning can be leveraged to enhance training efficiency. We provide reproducible code for all analyses and an open-source implementation of our method. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Clinical outcomes in patients with piperacillin/ tazobactam-non-susceptible but ceftriaxone-susceptible E. coli or K. pneumoniae bloodstream infection.

A small proportion of Escherichia coli and Klebsiella pneumoniae demonstrate in vitro non-susceptibility to piperacillin/tazobactam but retain susceptibility to ceftriaxone. Uncertainty remains regarding how best to treat these isolates. We sought to compare clinical outcomes between patients with piperacillin/tazobactam-non-susceptible but ceftriaxone-susceptible E. coli or K. pneumoniae bloodstream infection receiving definitive therapy with ceftriaxone versus an alternative effective antibiotic. We retrospectively identified patients with a positive blood culture for piperacillin/tazobactam-non-susceptible but ceftriaxone-susceptible E. coli or K. pneumoniae between 1 January 2013 and 31 December 2022. Patients were divided into one of two definitive treatment groups: ceftriaxone or alternative effective antibiotic. Our primary outcome was a composite of 90 day all-cause mortality, hospital readmission, or recurrence of infection. We used Cox proportional hazards models to compare time with the composite outcome between groups. Sixty-two patients were included in our analysis. Overall, median age was 63 years (IQR 49.5-71.0), the most common source of infection was intra-abdominal (25/62; 40.3%) and the median total duration of therapy was 12.0 days (IQR 9.0-16.8). A total of 9/22 (40.9%) patients in the ceftriaxone treatment group and 18/40 (45.0%) patients in the alternative effective antibiotic group met the composite endpoint. In an adjusted time-to-event analysis, there was no difference in the composite endpoint between groups (HR 0.67, 95% CI 0.30-1.50). The adjusted Bayesian posterior probability that the HR was less than or equal to 1 (i.e. ceftriaxone is as good or better than alternative therapy) was 85%. These findings suggest that ceftriaxone can be used to effectively treat bloodstream infections with E. coli or K. pneumoniae that are non-susceptible to piperacillin/tazobactam but susceptible to ceftriaxone.

A Bayesian Spatio-Temporal Modeling Approach to the Inverse Heat Conduction Problem

Abstract A wide range of science and engineering problems involve inferring the system behavior and physical models from indirect observations. These are known as inverse problems. Unlike forward problems (predictive models), inverse problems are difficult to solve due to ill-posedness arising from one or more of non-existence, non-uniqueness (existence of many solutions), or instability (small perturbations in the measurements emerging from noise could lead to large changes in the solution). Therefore, direct inversion of the inverse problem in complex real-world systems is impractical. Several modeling approaches have been developed and adopted in the literature. However, the existing approaches do not consider spatio-temporal relationships. Meanwhile, various real systems and processes exist whose inverse problems can be comfortably modeled using spatio-temporal modeling techniques. We present a spatio-temporal inverse model considering interaction effects for modeling this type of inverse problem. We adopted a Bayesian inference framework for the subsequent parameter estimation problem and developed a Gibbs sampling algorithm to sample from the posterior distribution of the parameter of interest. Finally, we evaluate the proposed methodology through case studies on a 1D heat simulation and pool boiling experiment, using multi-sensor thermocouple measurements to reconstruct heat flux. Compared to the conventional methods, the proposed method performs better and can be adopted for solving inverse heat conduction problems. Moreover, incorporating the spatio-temporal interaction term notably improved the model's performance.

Uncertainty analysis and economic value prediction of water environmental capacity based on Copula and Bayesian model: A case study of Yitong River, China

Water environmental capacity (WEC) is an indicator of environment management. The uncertainty analysis of WEC is more closely aligned with the actual conditions of the water body. It is crucial for accurately formulating pollution total emissions control schemes. However, the current WEC uncertainty analysis method ignored the connection between water quality and discharge, and required a large amount of monitoring data. This study analyzed the uncertainty of the WEC and predicted its economic value based on Copula and Bayesian model for the Yitong River in China. The Copula model was employed to calculate joint probabilities of water quality and discharge. And the posterior distribution of WEC with limited data was obtained by the Bayesian formula. The results showed that the WEC-COD in the Yitong River was 9009.67 t/a, while NH3-N had no residual WEC. Wanjinta Highway Bridge-Kaoshan Town reach had the most serious pollution. In order to make it have WEC, the reduction of COD and NH3-N was 5330.47 t and 3017.87 t. The economic value of WEC-COD was 5.97 107 CNY, and the treatment cost was 2.04 108 CNY to make NH3-N have residual WEC. The economic value distribution of WEC was extremely uneven, which could be utilized by adjusting the sewage outlet. In addition, since the treated water was discharged into the Sihua Bridge-Wanjinta Highway Bridge reach, the WEC-COD and the economic value were 19488.51 t/a and 8.24 107 CNY. Increasing the flow of rivers could effectively improve WEC and economic value. This study provided an evaluation tool for guiding river water environment management.

230 Using phenotypes with uncertain identification leveraging computer vision models

Abstract There has been an increase in high throughput phenotyping using sensors in livestock farming. The major obstacle when analyzing phenotypic data in studies using cameras is identifying the animals using computer vision. To overcome this problem, an analysis method that uses keypoint detection in conjunction with classification algorithms can identify animals in images. However, these algorithms generally output a prediction probability for different classes, reflecting uncertainty. In most practices, the highest probability will be assigned as the putative identification (ID). In some cases, this may introduce incorrect identification and affect downstream decisions. Here, we propose an analytical approach to incorporate all the uncertainty in the putative ID distribution into downstream inferences. We used a publicly available keypoint detection dataset of 17 keypoints detected in 3,226 images from 30 diverse thoroughbred horses. We performed feature engineering with Linear Discriminant Analysis (LDA) using 5-fold cross-validation to build horse classifiers. The best model selected had an accuracy of 88.12% to identify animals in the testing set. Afterward, we performed Monte Carlo simulations to add a phenotype that was assumed independent of the image keypoints. This generated a set of plasmodes to evaluate methods that accounted for ID uncertainty. Each simulation included different means for two groups of 15 horses. In total nine simulation scenarios were considered including varying effect sizes of group mean difference and various repeatability values (horse variance to total variance ratio). We analyzed the simulated data with three different mixed models: 1) Fixed and random effects were based on using ground truth assignment of ID, 2) Fixed and random effects were based on using the putative ID with the highest posterior probability from the best classifier, 3) Finally, prediction probabilities were used to account for putative ID uncertainty. As expected, the model that used ground truth ID had the highest power to detect group differences, and it had the lowest root mean square error (rMSE) to estimate variance components across all scenarios. The linear model that used highest posterior probability putative ID, showed the lowest power and highest rMSE. For instance, as shown in Table 1, the mean rMSE value for group mean differences in the model using ground truth is 0.01 and the model using highest posterior probabilities is 0.13. The model that incorporated ID uncertainty had intermediate properties, showing a rMSE of 0.03 for group differences. Overall, incorporating uncertainty in Animal ID results in improved performance in ultimate test statistics used in animal studies. This is very important in breeding and precision livestock farming contexts where animal identification is challenging. We will further investigate this.

62 Implications of factor analyses and Bayesian network learning to develop composite traits of size attributes in developing beef heifers

Abstract Advancements in phenotypic data collection methods provide opportunities to capture complex biological traits more efficiently. Even so, interpreting those phenotypes and using them in selection decisions is challenging in beef cattle production. Objectives of this study were to 1) develop composite phenotype(s) of economic importance in beef heifers given attributes of their body conformation and ultrasound-based measures of reproductive and carcass traits, and 2) identify associated genomic regions under selection using the structure equation modeling (SEM) approach. In this study, we hypothesized that the interplay of factor analysis and Bayesian Network Learning (BNL) approaches would better define the complex composite traits for size and carcass-related traits in beef heifers. Phenotypic data of admixed beef heifers (n = 336) for different reproductive, body conformational, and carcass-associated phenotypic traits (n = 16) were collected. Using exploratory and confirmatory factor analyses, two latent variables explaining 14 phenotypic traits were identified, herein called Body Size (BS; n = 7 traits represented) and Body Composition (BC; n = 7 traits represented). To efficiently model them in genome-wide association studies (GWAS), fixed effects of data collection year, dam age, and primary breed group as well as causal network structure identified through BNL were used within SEM model of GWAS. Body Size was directly or indirectly contributing to BC based on BNL structure. Given the data-driven approach and novelty of the data, BayesCπ model was applied with the sliding window of 1 Mb to avoid noise and linkage disequilibrium implications. Regions capturing large amounts of genetic variance (Posterior Probability of Association, PPA ≥ 0.8) were mapped to their closest gene for enrichment analysis. Twenty-four different genes were identified associated with both composite phenotypes. Functional enrichment analysis of significantly involved pathways (FDR < 0.05) indicated that genes from the HERC family (HERC3, HERC6, and HERC5) were found involved in cellular growth, and those from desmosomes (DC2, and DC3) were involved in morphogenesis like BS. Genes related to energy metabolism like IMPAD1, and PIGY, along with genes associated with traits like muscular development, and other carcass traits (i.e., FAM184B, NCAPG, and LCORL), were also found associated with BS and BC. Heritability ranged from 0.551 ± 0.048 to 0.897 ± 0.145 for BS and BC, respectively. The combined heritability of reproductive and carcass traits (BC) was much greater than the heritability of any related individual trait reported to date and was comparable for individual BS-related traits. Given this, we conclude that the implications of factor analyses with BNL can provide better biological insights for improved genomic selection in beef cattle, even for traits of low heritability.

Bayesian inference of vorticity in unbounded flow from limited pressure measurements

We study the instantaneous inference of an unbounded planar flow from sparse noisy pressure measurements. The true flow field comprises one or more regularized point vortices of various strength and size. We interpret the true flow's measurements with a vortex estimator, also consisting of regularized vortices, and attempt to infer the positions and strengths of this estimator assuming little prior knowledge. The problem often has several possible solutions, many due to a variety of symmetries. To deal with this ill posedness and to quantify the uncertainty, we develop the vortex estimator in a Bayesian setting. We use Markov-chain Monte Carlo and a Gaussian mixture model to sample and categorize the probable vortex states in the posterior distribution, tailoring the prior to avoid spurious solutions. Through experiments with one or more true vortices, we reveal many aspects of the vortex inference problem. With fewer sensors than states, the estimator infers a manifold of equally possible states. Using one more sensor than states ensures that no cases of rank deficiency arise. Uncertainty grows rapidly with distance when a vortex lies outside of the vicinity of the sensors. Vortex size cannot be reliably inferred, but the position and strength of a larger vortex can be estimated with a much smaller one. In estimates of multiple vortices their individual signs are discernible because of the nonlinear coupling in the pressure. When the true vortex state is inferred from an estimator of fewer vortices, the estimate approximately aggregates the true vortices where possible.

A Practical probabilistic history matching framework for characterizing fracture network parameters of shale gas reservoirs

Simulating fluid flow in complex fracture systems encountered in subsurface systems, such as those encountered in tight or shale gas reservoirs, presents considerable challenges. Sophisticated numerical techniques and characterization approaches are required to integrate static and dynamic data from multiple sources to account for the intricate interplay between fractures and the surrounding rock matrix. A novel workflow using an indicator-based probability perturbation method (PPM) is applied to characterize uncertain discrete fracture network parameters. The posterior probability distributions of primary and secondary fracture transmissivity, aperture, length, height, and intensity of the secondary fracture are estimated according to the production (flow) histories. A case study based on the Horn River shale gas reservoir is presented. A pilot point scheme is formulated to update the distributions of P32L. The forward modelling entails upscaling each fracture model into a dual-porosity dual-permeability model and performing multiphase flow simulation. This approach produces an ensemble of history-matched discrete fracture and upscaled models by incorporating static and dynamic data. The results demonstrate the utility of the developed method for estimating secondary fracture parameters, which are not inferrable from other static information alone. The inference of both primary and secondary fractures offers a more comprehensive characterization of the fracture networks in tight or shale gas reservoirs. Integrating a pilot point parameterization technique and a sequential simulation approach into the PPM framework presents significant novelties in contributing to a comprehensive and effective method of simulating fluid flow in complex fracture systems, addressing the challenges associated with non-linear relationships between fracture model parameters and the corresponding flow response.

Online detection of the incidence via transfer learning

AbstractThe counting process has abundant applications in reality, and Poisson process monitoring actually has received extensive attention and research. However, conventional methods experience poor performance when shifts appears early and only small number of historical observations in Phase I can be used for estimation. To overcome it, we creatively propose a new online monitoring algorithm under the transfer learning framework, which utilizes the information from observations of additional data sources so that the target process can be described better. By making the utmost of the somewhat correlated data from other domains, which is measured by a bivariate Gamma distributed statistic presented by us, the explicit properties (e.g., posterior probability mass function, posterior expectation, and posterior variance) are also strictly proved. Furthermore, based on the above theoretical results, we design two computationally efficient control schemes in Phase II, that is a control chart based on the cumulative distribution function for large shifts and an exponentially weighted moving average control chart for small shifts. For a better understanding of the more practical applications and transferability matter, we provide some optimal values for parameter setting. Extensive numerical simulations and a case of skin cancer incidence in America verify the superiorities of our approach.