Bayesian networks: generating independent samples Minh, Do Le Paul
Communications in statistics. Simulation and computation,
12/2023, Letnik:
ahead-of-print, Številka:
ahead-of-print
Journal Article
Recenzirano
Given a Bayesian network with evidence, we propose an efficient majorizing function for the Acceptance-Rejection method to generate a sequence of independent samples from the posterior distribution. ...Because the proposed method generates independent samples, it has none of the problems associated with MCMC. The sequence allows for the easy calculations of the confidence intervals for the posterior quantities of interest. It can also yield independent inputs for the simulations of a bigger probabilistic system that includes the BN.
Understanding the spatiotemporal processes governing Cd behavior at the soil-solution-root interface is crucial for developing effective remediation strategies. This study examined the processes of ...chemical remediation in Cd-contaminated paddy soil using rhizotrons over the entire rice growth period. One-dimensional profile sampling with a 10 cm resolution revealed that during the initial flooding, paddy soil was strongly stimulated, followed by stabilization of porewater properties. X-ray diffraction of freeze-dried porewater confirmed the generation of submicron-precipitates such as CdS under continuous flooding, resulting in low ion levels of water-soluble Cd (<1 μg/L) and sulfate (<10 mg/L) in porewater. Two-dimensional imaging technologies indicated the maximum iron‑manganese plaque (IP) within 20–110 μm of the root surface. Subsequently, monitoring O2 in the rhizosphere with a planar optode by two 100 cm2 membranes for a consecutive month revealed significant circadian O2 variations between the root base and tip. Destructive sampling results showed that acid-soluble Cd in soils, as available Cd, is crucial for Cd uptake by rice roots under continuous flooding. The IP deposited on the root surface, as the barriers of Cd translocation, increased with rice growth and blocked Cd translocation from soil to rice by about 18.11 %–25.43 % at maturity. A Si-Ca-Mg compound amendment reduced available Cd by about 10 % and improved Cd blocking efficiency by about 7.32 % through increasing IP concentration, resulting in the absorption ratio of Cd in the amendment group being half that of the control group. By unveiling the complex Cd interactions at the soil-rice interface, this study lays the groundwork for developing effective agricultural practices to mitigate Cd-contaminated paddy and ensure food safety.
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•CdS precipitation reduced Cd activity during flooding.•Amendment (FSY) promoted Cd conversion to stable fractions better than flooding.•O2 fluctuations in root tip benefited iron-manganese plaque (IP) deposition.•FSY application increased IP efficiency in blocking Cd.
This study aimed to explore how travelling in different group constellations (alone, with known or with unknown people) affects children's and adults’ visual behaviour in traffic when cycling or ...walking. Additionally, mobile phone/earphone usage was considered, too. A follow-along study (n = 43) and an observation study (n = 898) were conducted to observe travellers in a natural setting. In the follow-along study, eye-tracking was used to investigate children's glances behaviour on their way to school and how well they manage to fulfil attentional requirements. The observational study focused on children's and adults' visual behaviour at several intersections. The main result of the study was that group membership appears to have a large influence on individuals' visual sampling strategy. In formal groups reliance on each other was found to be stronger than in informal groups. People with a natural responsibility in the group, such as parents or other adults, take a more active role in visual monitoring. Reliance on others is found to a greater extent among pedestrians than cyclists. Regarding communication devices, the use of earphones did not significantly affect glance behaviour towards relevant areas. In naturalistic situations, group constellation, age and phone/earphone usage are interlinked, which needs to be considered when studying these factors.
Insufficient signal-to-noise ratio (SNR) limits the quality of cardiac pulses measured by multitap CMOS image sensors (CISs). We present an in-pixel temporal redundant samplings (TRS) method for ...enhancing the SNR through sampling the charges originated by one individual optical scene into the multiple storage taps in turn with the identical time window, enabling magnification of the image signal in shot noise domain. Modeling indicates that the linear SNR increases with a factor square root of TRS sampling number, which is also verified by characterization results utilizing a four-tap CIS. Application results show that the high-frequency (HF) noise components observed in the measured cardiac pulses can be attenuated by 67.9%, 48%, and 53.6% with heart rate detection accuracies up to 99.7%, 99.3%, and 98.7% by applying TRS modes under given conditions of stable ambient, background light (BGL) variation, and motion disturbance, respectively. Moreover, the proposed method is as well foreseeable to be extended to other multitap-involved imaging applications.
Context: Ranking-oriented defect prediction (RODP) ranks software modules to allocate limited testing resources to each module according to the predicted number of defects. Most RODP methods overlook ...that ranking a module with more defects incorrectly makes it difficult to successfully find all of the defects in the module due to fewer testing resources being allocated to the module, which results in much higher costs than incorrectly ranking the modules with fewer defects, and the numbers of defects in software modules are highly imbalanced in defective software datasets. Cost-sensitive learning is an effective technique in handling the cost issue and data imbalance problem for software defect prediction. However, the effectiveness of cost-sensitive learning has not been investigated in RODP models. Aims: In this article, we propose a cost-sensitive ranking support vector machine (SVM) (CSRankSVM) algorithm to improve the performance of RODP models. Method: CSRankSVM modifies the loss function of the ranking SVM algorithm by adding two penalty parameters to address both the cost issue and the data imbalance problem. Additionally, the loss function of the CSRankSVM is optimized using a genetic algorithm. Results: The experimental results for 11 project datasets with 41 releases show that CSRankSVM achieves 1.12%-15.68% higher average fault percentile average (FPA) values than the five existing RODP methods (i.e., decision tree regression, linear regression, Bayesian ridge regression, ranking SVM, and learning-to-rank (LTR)) and 1.08%-15.74% higher average FPA values than the four data imbalance learning methods (i.e., random undersampling and a synthetic minority oversampling technique; two data resampling methods; RankBoost, an ensemble learning method; IRSVM, a CSRankSVM method for information retrieval). Conclusion: CSRankSVM is capable of handling the cost issue and data imbalance problem in RODP methods and achieves better performance. Therefore, CSRankSVM is recommended as an effective method for RODP.
In the context of geotechnical uncertainty characterization, the reliability‐based design optimization (RBDO) provides an effective means to achieve the most balanced design outcome between the ...targeted safety and the risk‐based cost planning. As a common practice, the geotechnical RBDO is conducted on the premise of prescribed statistical information of those uncertain parameters. Nevertheless, the uncertain geotechnical parameters are usually subject to amendment resulting from further site investigation. In other words, changing design scenarios must be properly handled in advanced geotechnical RBDO. Thus, in this study, a novel reliability updating method using a sample reweighting technique that requires only a single simulation run is developed for RBDO. Simultaneously, the RBDO is readily extendable to RBDO updating under changing design scenarios while avoiding any further re‐evaluation of the performance functions. The proposed RBDO updating method allows for the consideration of a continuous design space encompassing an infinite number of feasible designs, whereas the existing simulation‐based method can only consider a finite number of feasible designs in a discrete design space. Moreover, the efficiency and flexibility of the proposed RBDO updating method are illustrated through a standard RBDO test problem and a practical rock slope design example, where the changing design scenarios of different means, standard deviations, correlation coefficient, and distributions are respectively studied.
Information theory has shown a notable performance in the field of computer vision (CV) and natural language processing (NLP), therefore, many works start to learn better node-level and graph-level ...representations in the information theory perspective. Previous works have shown great performance by maximizing the mutual information between graph and node representation to capture graph information. However, a simple mixture of information in a single node representation leads to a lack of information related to the graph structure, which leads to the information gap between model and theoretical optimal solution. To solve this problem, we propose to replace the node representation with subgraph representation to reduce the information gap between the model and the optimal case. And to capture enough information of original graph, three operators (information aggregators): attribute-conv, layer-conv and subgraph-conv, are designed to gather information from different aspects, respectively. Moreover, to generate more expressive subgraphs, we propose a universal framework to generate subgraphs autoregressively, which provides a comprehensive understanding of the graph structure in a learnable way. We proposed a Head–Tail negative sampling method to provide more negative samples for more efficient and effective contrastive learning. Moreover, all these components can be plugged into any existed Graph Neural Networks. Experimentally, we achieve new state-of-the-art results in several benchmarks under the unsupervised case. We also evaluate our model on semi-supervised learning tasks and make a fair comparison to state-of-the-art semi-supervised methods.
In this paper, we propose a new aperiodic formulation of model predictive control for nonlinear continuous-time systems. Unlike earlier approaches, we provide event-triggered conditions without using ...the optimal cost as a Lyapunov function candidate. Instead, we evaluate the time interval when the optimal state trajectory enters a local set around the origin. The obtained event-triggered strategy is more suitable for practical applications than the earlier approaches in two directions. First, it does not include parameters (e.g., Lipschitz constant parameters of stage and terminal costs) which may be a potential source of conservativeness for the event-triggered conditions. Second, the event-triggered conditions are necessary to be checked only at certain sampling time instants, instead of continuously. This leads to the alleviation of the sensing cost and becomes more suitable for practical implementations under a digital platform. The proposed event-triggered scheme is also validated through numerical simulations.
Abstract Despite successful use in a wide variety of disciplines for data analysis and prediction, machine learning (ML) methods suffer from a lack of understanding of the reliability of predictions ...due to the lack of transparency and black-box nature of ML models. In materials science and other fields, typical ML model results include a significant number of low-quality predictions. This problem is known to be particularly acute for target systems which differ significantly from the data used for ML model training. However, to date, a general method for uncertainty quantification (UQ) of ML predictions has not been available. Focusing on the intuitive and computationally efficient similarity-based UQ, we show that a simple metric based on Euclidean feature space distance and sampling density together with the decorrelation of the features using Gram–Schmidt orthogonalization allows effective separation of the accurately predicted data points from data points with poor prediction accuracy. To demonstrate the generality of the method, we apply it to support vector regression models for various small data sets in materials science and other fields. We also show that this metric is a more effective UQ tool than the standard approach of using the average distance of k nearest neighbors ( k = 1–10) in features space for similarity evaluation. Our method is computationally simple, can be used with any ML learning method and enables analysis of the sources of the ML prediction errors. Therefore, it is suitable for use as a standard technique for the estimation of ML prediction reliability for small data sets and as a tool for data set design.
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