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Modeling three sources of uncertainty in assisted reproductive technologies with probabilistic graphical models

COMPUTERS IN BIOLOGY AND MEDICINE 2022年 150卷 106160-106160页

作者： Hernandez-Gonzalez, Jeronimo Valls, Olga Torres-Martin, Adrian Cerquides, Jesus Univ Barcelona UB Dept Matemat &Informat Barcelona 08007 Spain Univ Autonoma Barcelona Dept Informat & Commun Engn Cerdanyola Del Valles 08193 Spain Artificial Intelligence Res Inst IIIA CSIC Bellaterra 08193 Spain

Embryo selection is a critical step in assisted reproduction: good selection criteria are expected to increase the probability of inducing a pregnancy. Machine learning techniques have been applied for implantation prediction or embryo quality assessment, which embryologists can use to make a decision about embryo selection. However, this is a highly uncertain real-world problem, and current proposals do not model always all the sources of uncertainty. We present a novel probabilistic graphical model that accounts for three different sources of uncertainty, the standard embryo and cycle viability, and a third one that represents any unknown factor that can drive a treatment to a failure in otherwise perfect conditions. We derive a parametric learning method based on the Expectation-Maximization strategy, which accounts for uncertainty issues. We empirically analyze the model within a real database consisting of 604 cycles (3125 embryos) carried out at Hospital Donostia (Spain). Embryologists followed the protocol of the Spanish Association for Reproduction Biology Studies (ASEBIR), based on morphological features, for embryo selection. Our model predictions are correlated with the ASEBIR protocol, which validates our model. The benefits of accounting for the different sources of uncertainty and the importance of the cycle characteristics are shown. Considering only transferred embryos, our model does not further discriminate them as implanted or failed, suggesting that the ASEBIR protocol could be understood as a thorough summary of the available morphological features.

关键词： Assisted reproductive technologies Embryo selection Machine learning probabilistic graphical models Expectation-Maximization

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PGMax: factor graphs for discrete probabilistic graphical models and loopy belief propagation in JAX

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 18192-18216页

作者： Guangyao Zhou Antoine Dedieu Nishanth Kumar Wolfgang Lehrach Shrinu Kushagra Dileep George Miguel Lázaro-Gredilla Google DeepMind Massachusetts Institute of Technology

PGMax is an open-source Python/ JAX package for (a) easily specifying discrete probabilistic graphical models (PGMs) as factor graphs; and (b) automatically running efficient and scalable differentiable Loopy Belief Propagation (LBP). PGMax supports general factor graphs with tractable factors, and leverages modern accelerators like GPUs for inference. Compared with alternative libraries, PGMax obtains higher-quality inference results with up to three orders-of-magnitude inference time speedups. PGMax interacts seamlessly with the growing JAX ecosystem, opening up new research possibilities. Our source code, examples and documentation are available at https://***/google-deepmind/PGMax.

关键词： probabilistic graphical models Bayesian inference belief propagation JAX

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Detecting COVID-19 Utilizing probabilistic graphical models

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第6期12卷 786-793页

作者： Alsuwat, Emad Alzahrani, Sabah Alsuwat, Hatim Taif Univ Coll Comp & Informat Technol Dept Comp Sci At Taif Saudi Arabia Umm Al Qura Univ Coll Comp & Informat Syst Dept Comp Sci Mecca Saudi Arabia

probabilistic graphical models are employed in a variety of areas such as artificial intelligence and machine learning to depict causal relations among sets of random variables. In this research, we employ probabilistic graphical models in the form of Bayesian network to detect coronavirus disease 2019 (denoted as COVID-19) disease. We propose two efficient Bayesian network models that are potent in encoding causal relations among random variable, i.e., COVID-19 symptoms. The first Bayesian network model, denoted as BN1, is built depending on the acquired knowledge from medical experts. We collect data from clinics and hospitals in Saudi Arabia for our research. We name this authentic dataset DScovid. The second Bayesian network model, denoted as BN2, is learned from the real dataset DScovid depending on Chow-Liu tree approach. We also implement our proposed Bayesian network models and present our experimental results. Our results show that the proposed approaches are capable of modeling the issue of making decisions in the context of COVID-19. Moreover, our experimental results show that the two Bayesian network models we propose in this work are effective for not only extracting casual relations but also reducing uncertainty and increasing the effectiveness of causal reasoning and prediction.

关键词： Coronavirus disease 2019 COVID-19 artificial intelligence machine learning probabilistic graphical models causal models Bayesian networks detection methods

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Robust Depth Image Acquisition Using Modulated Pattern Projection and probabilistic graphical models

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SENSORS 2016年第10期16卷 1740-1740页

作者： Kravanja, Jaka Zganec, Mario Zganec-Gros, Jerneja Dobrisek, Simon Struc, Vitomir Alpineon Doo Ulica Iga Grudna 15 SI-1000 Ljubljana Slovenia Univ Ljubljana Fac Elect Engn Trzaska Cesta 25 SI-1000 Ljubljana Slovenia

Depth image acquisition with structured light approaches in outdoor environments is a challenging problem due to external factors, such as ambient sunlight, which commonly affect the acquisition procedure. This paper presents a novel structured light sensor designed specifically for operation in outdoor environments. The sensor exploits a modulated sequence of structured light projected onto the target scene to counteract environmental factors and estimate a spatial distortion map in a robust manner. The correspondence between the projected pattern and the estimated distortion map is then established using a probabilistic framework based on graphical models. Finally, the depth image of the target scene is reconstructed using a number of reference frames recorded during the calibration process. We evaluate the proposed sensor on experimental data in indoor and outdoor environments and present comparative experiments with other existing methods, as well as commercial sensors.

关键词： depth imaging modulated acquisition structured light triangulation probabilistic graphical models 3D reconstruction

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MAsCOT: Self-adaptive Opportunistic Offloading for Cloud-Enabled Smart Mobile Applications with probabilistic graphical models at Runtime

MAsCOT: Self-adaptive Opportunistic Offloading for Cloud-Ena...

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Hawaii International Conference on System Sciences

作者： Nayyab Zia Naqvi Jonas Devlieghere Davy Preuveneers Yolande Berbers iMinds-DistriNet KU Leuven

ISBN: (纸本)9781509019816

Although extensive progress has been made in Mobile Cloud Augmentation, automated decision support on the device that enables the opportunistic and intelligent use of cloud resources is missing. Furthermore, we need solutions with reflective capabilities that can handle a changing environment and runtime variability. To simplify the deployment of smart mobile applications, we present a framework with retrospective decision support based on reinforcement learning to cater for various resource-performance trade-offs. We have adopted the MAPE-K (Monitor-Analyse-Plan-Execute-Knowledge) control loop architecture and realized the loop with Dynamic Decision Networks to manage self-adaptation at runtime. Our experiments show that our framework is capable of intelligently inferring appropriate decisions with an acceptable performance overhead of 10 milliseconds on mobile devices.

关键词： Mobile cloud computing Decision support probabilistic graphical models Dynamic offloading models@runtime decision making support Runtime Cloud Computing Mobile Applications self-adaptive probabilistic Model Mascot Scoring Engine UNLOADING graphical models Frameworks learning (artificial intelligence) Mobile devices

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Tracking Performance and Forming Study Groups for Prep Courses Using probabilistic graphical models 16

Tracking Performance and Forming Study Groups for Prep Cours...

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International Conference on Autonomous Agents and Multiagent Systems

作者： Yoram Bachrach Yoad Lewenberg Jeffrey S. Rosenschein Yair Zick Microsoft Research Hebrew Univ. of Jerusalem Carnegie Mellon Univ.

ISBN: (纸本)9781510855083

Efficient tracking of class performance across topics is an important aspect of classroom teaching; this is especially true for psychometric general intelligence exams, which test a varied range of abilities. We develop a framework that uncovers a hidden thematic structure underlying student responses to a large pool of questions, using a probabilistic graphical model.

关键词： Experimentation Theory probabilistic graphical models Bayesian PCA Education experimentation visual tracking graphical models Education Probability theories Curriculum

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Reusability of Bayesian Networks case studies: a survey

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APPLIED INTELLIGENCE 2025年第6期55卷 1-25页

作者： Babakov, Nikolay Sivaprasad, Adarsa Reiter, Ehud Bugarin-Diz, Alberto Univ Santiago Compostela Ctr Singular Invest Tecnoloxias Intelixentes CiTIU Santiago De Compostela 15782 Spain Univ Aberdeen Dept Comp Sci Aberdeen Scotland

Bayesian Networks (BNs) are probabilistic graphical models used to represent variables and their conditional dependencies, making them highly valuable in a wide range of fields, such as radiology, agriculture, neuroscience, construction management, medicine, and engineering systems, among many others. Despite their widespread application, the reusability of BNs presented in papers that describe their application to real-world tasks has not been thoroughly examined. In this paper, we perform a structured survey on the reusability of BNs using the PRISMA methodology, analyzing 147 papers from various domains. Our results indicate that only 18% of the papers provide sufficient information to enable the reusability of the described BNs. This creates significant challenges for other researchers attempting to reuse these models, especially since many BNs are developed using expert knowledge elicitation. Additionally, direct requests to authors for reusable BNs yielded positive results in only 12% of cases. These findings underscore the importance of improving reusability and reproducibility practices within the BN research community, a need that is equally relevant across the broader field of Artificial Intelligence.

关键词： Bayesian networks probabilistic graphical models Reproducibility Reusability

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bnRep: A repository of Bayesian networks from the academic literature

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NEUROCOMPUTING 2025年 624卷

作者： Leonelli, Manuele IE Univ Sch Sci & Technol Madrid Spain

Bayesian networks (BNs) are widely used for modeling complex systems with uncertainty, yet repositories of pre-built BNs remain limited. This paper introduces bnRep, an open-source R package offering a comprehensive collection of documented BNs, facilitating benchmarking, replicability, and education. With over 200 networks from academic publications, bnRep integrates seamlessly with bnlearn and other R packages, providing users with interactive tools for network exploration. The package includes a Shiny app for intuitive and interactive filtering and visualization, further enhancing accessibility for users of all expertise levels.

关键词： Bayesian networks probabilistic graphical models R package Repository

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Lifting factor graphs with some unknown factors for new individuals

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2025年 179卷

作者： Luttermann, Malte Moeller, Ralf Gehrke, Marcel German Res Ctr Artificial Intelligence DFKI Ratzeburger Allee 160 D-23562 Lubeck Germany Univ Hamburg Inst Humanities Ctr Artificial Intelligence Warburgstr 28 D-20354 Hamburg Germany

Lifting exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, allowing to carry out query answering more efficiently while maintaining exact answers. In this paper, we investigate how lifting enables us to perform probabilistic inference for factor graphs containing unknown factors, i.e., factors whose underlying function of potential mappings is unknown. We present the Lifting Factor Graphs with Some Unknown Factors (LIFAGU) algorithm to identify indistinguishable subgraphs in a factor graph containing unknown factors, thereby enabling the transfer of known potentials to unknown potentials to ensure a well-defined semantics of the model and allow for (lifted) probabilistic inference. We further extend LIFAGU to incorporate additional background knowledge about groups of factors belonging to the same individual object. By incorporating such background knowledge, LIFAGU is able to further reduce the ambiguity of possible transfers of known potentials to unknown potentials.

关键词： probabilistic graphical models Factor graphs Lifted inference

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Toward Variational Structural Learning of Bayesian Networks

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IEEE ACCESS 2025年 13卷 26130-26141页

作者： Masegosa, Andres R. Gomez-Olmedo, Manuel Aalborg Univ Dept Comp Sci DK-9220 Aalborg Denmark Univ Granada Dept Comp Sci & Artificial Intelligence Granada 18012 Spain

This study presents a novel variational framework for structural learning in Bayesian networks (BNs), addressing the key limitation of existing Bayesian methods: their lack of scalability to large graphs with many variables. Traditional approaches, such as MCMC and stochastic search, often encounter computational barriers due to the super-exponential growth of the Directed Acyclic Graph (DAG) space. Our method introduces a scalable alternative by leveraging a factorized variational family to approximate the posterior distribution over DAG structures, enabling efficient computation of Bayesian scores and predictive posterior inference. Unlike previous methods, which are constrained by high computational costs or domain-specific limitations, this approach achieves tractability through mean-field variational inference and tractable updating equations, allowing application to significantly larger datasets. Empirical results on benchmark datasets demonstrate that the proposed framework consistently outperforms state-of-the-art methods in terms of scalability and predictive accuracy while maintaining robustness across diverse scenarios. This work represents a key step towards scalable Bayesian structural learning and opens avenues for future research to refine the variational approximation and incorporate advanced parallelization techniques.

关键词： Bayesian networks probabilistic graphical models structural learning variational methods Bayesian networks probabilistic graphical models structural learning variational methods

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