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The impact of prior knowledge on causal structure learning

KNOWLEDGE AND INFORMATION SYSTEMS 2023年第8期65卷 3385-3434页

作者： Constantinou, Anthony C. Guo, Zhigao Kitson, Neville K. Queen Mary Univ London QMUL Sch Elect Engn & Comp Sci Bayesian Artificial Intelligence Res Lab Machine Intelligence & Decis Syst MInDS Res Grp London E1 4NS England

Causal Bayesian networks have become a powerful technology for reasoning under uncertainty in areas that require transparency and explainability, by relying on causal assumptions that enable us to simulate hypothetical interventions. The graphical structure of such models can be estimated by structure learning algorithms, domain knowledge, or a combination of both. Various knowledge approaches have been proposed in the literature that enables us to specify prior knowledge that constrains or guides these algorithms. This paper introduces some novel, and also describes some existing, knowledge-based approaches that enable us to combine structure learning with knowledge obtained from heterogeneous sources. We investigate the impact of these approaches on structure learning across different algorithms, case studies and settings that we might encounter in practice. Each approach is assessed in terms of effectiveness and efficiency, including graphical accuracy, model fitting, complexity, and runtime;making this the first paper that provides a comparative evaluation of a wide range of knowledge approaches for structure learning. Because the value of knowledge depends on what data are available, we illustrate the results both with limited and big data. While the overall results show that knowledge becomes less important with big data due to higher learning accuracy rendering knowledge less important, some of the knowledge approaches are found to be more important with big data. Amongst the main conclusions is the observation that reduced search space obtained from knowledge does not always imply reduced computational complexity, perhaps because the relationships implied by the data and knowledge are in tension.

关键词： Bayesian networks Causal discovery Directed acyclic graphs Domain knowledge Knowledge-based constraints probabilistic graphical models

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Structured probabilistic inference

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2012年第7期53卷 946-968页

作者： Wuillemin, Pierre-Henri Torti, Lionel UPMC Lab Informat Paris 6 F-75005 Paris France

probabilistic inference is among the main topics with reasoning in uncertainty in AI. For this purpose, Bayesian Networks (BNs) is one of the most successful and efficient probabilistic graphical Model (PGM) so far. Since the mid-90s, a growing number of BNs extensions have been proposed. Object-oriented, entity-relationship and first-order logic are the main representation paradigms used to extend BNs. While entity-relationship and first-order models have been successfully used for machine learning in defining lifted probabilistic inference, object-oriented models have been mostly underused. Structured inference, which exploits the structural knowledge encoded in an object-oriented PGM, is a surprisingly unstudied technique. In this paper we propose a full object-oriented framework for probabilistic Relational models (PRMs) and propose two extensions of the state-of-the-art structured inference algorithm: SPI which removes the major flaws of existing algorithms and SPISBB which largely enhances SPI by using d-separation. (C) 2012 Published by Elsevier Inc.

关键词： Bayesian Networks probabilistic graphical models probabilistic Relational models Object Oriented Bayesian Networks probabilistic inference Structured inference

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Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2013年第3期57卷 185-195页

作者： Hernandez-Leal, Pablo Rios-Flores, Alma Avila-Rios, Santiago Reyes-Teran, Gustavo Gonzalez, Jesus A. Fiedler-Cameras, Lindsey Orihuela-Espina, Felipe Morales, Eduardo F. Sucar, L. Enrique Inst Nacl Astrofis Opt & Electr Puebla Mexico Inst Nacl Enfermedades Respiratorias Ctr Invest Enfermedades Infecciosas Mexico City DF Mexico

Objective: The human immunodeficiency virus (HIV) is one of the fastest evolving organisms in the planet. Its remarkable variation capability makes HIV able to escape from multiple evolutionary forces naturally or artificially acting on it, through the development and selection of adaptive mutations. Although most drug resistance mutations have been well identified, the dynamics and temporal patterns of appearance of these mutations can still be further explored. The use of models to predict mutational pathways as well as temporal patterns of appearance of adaptive mutations could greatly benefit clinical management of individuals under antiretroviral therapy. Methods and material: We apply a temporal nodes Bayesian network (TNBN) model to data extracted from the Stanford HIV drug resistance database in order to explore the probabilistic relationships between drug resistance mutations and antiretroviral drugs unveiling possible mutational pathways and establishing their probabilistic-temporal sequence of appearance. Results: In a first experiment, we compared the TNBN approach with other models such as static Bayesian networks, dynamic Bayesian networks and association rules. TNBN achieved a 64.2% sparser structure over the static network. In a second experiment, the TNBN model was applied to a dataset associating antiretroviral drugs with mutations developed under different antiretroviral regimes. The learned models captured previously described mutational pathways and associations between antiretroviral drugs and drug resistance mutations. Predictive accuracy reached 90.5%. Conclusion: Our results suggest possible applications of TNBN for studying drug-mutation and mutation mutation networks in the context of antiretroviral therapy, with direct impact on the clinical management of patients under antiretroviral therapy. This opens new horizons for predicting HIV mutational pathways in immune selection with relevance for antiretroviral drug development and therapy p

关键词： probabilistic graphical models probabilistic learning Human immunodeficiency virus mutations

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Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear

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APPLIED INTELLIGENCE 2024年第2期54卷 1734-1750页

作者： Leonelli, Manuele Varando, Gherardo IE Univ Sch Sci & Technol Madrid Spain Univ Valencia Image Proc Lab Valencia Spain

Bayesian networks are widely used to learn and reason about the dependence structure of discrete variables. However, they can only formally encode symmetric conditional independence, which is often too strict to hold in practice. Asymmetry-labeled DAGs have been recently proposed to extend the class of Bayesian networks by relaxing the symmetric assumption of independence and denoting the dependence between the variables of interest. Here, we introduce novel structural learning algorithms for this class of models, which, whilst efficient, allow for a straightforward interpretation of the underlying dependence structure. A comprehensive computational study highlights the efficiency of the algorithms. A real-world data application using data from the Fear of COVID-19 Scale collected in Italy showcases their use in practice.

关键词： Bayesian networks Conditional independence probabilistic graphical models Staged trees Structural learning

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A review of representation issues and modeling challenges with influence diagrams

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2011年第3期39卷 227-241页

作者： Bielza, Concha Gomez, Manuel Shenoy, Prakash P. Univ Kansas Sch Business Lawrence KS 66045 USA Univ Politecn Madrid Dept Inteligencia Artificial E-28660 Madrid Spain Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

Since their introduction in the mid 1970s, influence diagrams have become a de facto standard for representing Bayesian decision problems. The need to represent complex problems has led to extensions of the influence diagram methodology designed to increase the ability to represent complex problems. In this paper, we review the representation issues and modeling challenges associated with influence diagrams. In particular, we look at the representation of asymmetric decision problems including conditional distribution trees, sequential decision diagrams, and sequential valuation networks. We also examine the issue of representing the sequence of decision and chance variables, and how it is done in unconstrained influence diagrams, sequential valuation networks, and sequential influence diagrams. We also discuss the use of continuous chance and decision variables, including continuous conditionally deterministic variables. Finally, we discuss some of the modeling challenges faced in representing decision problems in practice and some software that is currently available. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Decision-making under uncertainty Influence diagrams probabilistic graphical models Sequential decision diagrams Unconstrained influence diagrams Sequential valuation networks Sequential influence diagrams Partial influence diagrams Limited memory influence diagrams Gaussian influence diagrams Mixture of Gaussians influence diagrams Mixture of truncated exponentials influence diagrams Mixture of polynomials influence diagrams

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A Structured and Linguistic Approach to Understanding Recovery and Relapse in AA

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ACM TRANSACTIONS ON THE WEB 2021年第1期15卷 1–35页

作者： Bailey, Shawn Zhang, Yue Ramesh, Arti Golbeck, Jennifer Getoor, Lise SUNY Binghamton POB 6000 Binghamton NY 13902 USA Univ Maryland 4105E Hornbake BldgSouth Wing College Pk MD 20742 USA Univ Calif Santa Cruz Engn 2Room 341BMS SOE31156 High St Santa Cruz CA 95064 USA

Alcoholism, also known as Alcohol Use Disorder (AUD), is a serious problem affecting millions of people worldwide. Recovery from AUD is known to be challenging and often leads to relapse at various points after enrolling in a rehabilitation program such as Alcoholics Anonymous (AA). In this work, we present a structured and linguistic approach using hinge-loss Markov random fields (HL-MRFs) to understand recovery and relapse from AUD using social media data. We evaluate ourmodels on AA-attending users extracted from: (i) the Twitter social network and predict recovery at two different points-90 days and 1 year after the user joins AA, respectively, and (ii) the Reddit AA recovery forums and predict whether the participating user is currently sober. The two datasets present two facets of the same underlying problem of understanding recovery and relapse in AUD users. We flesh out different characteristics in both these datasets: (i) In the Twitter dataset, we focus on the social aspect of the users and the relationship with recovery and relapse, and (ii) in the Reddit dataset, we focus on modeling the linguistic topics and dependency structure to understand users' recovery journey. We design a unified modeling framework using HL-MRFs that takes the different characteristics of both these platforms into account. Our experiments reveal that our structured and linguistic approach is helpful in predicting recovery in users in both these datasets. We perform extensive quantitative analysis of different groups of features and dependencies among them in both datasets. The interpretable and intuitive nature of our models and analysis is helpful in making meaningful predictions and can potentially be helpful in identifying and preventing relapse early.

关键词： Social media analysis probabilistic graphical models modeling recovery from alcoholism alcoholics anonymous

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VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization

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BMC BIOINFORMATICS 2017年第1期18卷 1-18页

作者： Bolgar, Bence Antal, Peter Budapest Univ Technol & Econ Dept Measurement & Informat Syst Magyar Tudosok Krt 2 H-1117 Budapest Hungary

Background: Computational fusion approaches to drug-target interaction (DTI) prediction, capable of utilizing multiple sources of background knowledge, were reported to achieve superior predictive performance in multiple studies. Other studies showed that specificities of the DTI task, such as weighting the observations and focusing the side information are also vital for reaching top performance. Method: We present Variational Bayesian Multiple Kernel Logistic Matrix Factorization (VB-MK-LMF), which unifies the advantages of (1) multiple kernel learning, (2) weighted observations, (3) graph Laplacian regularization, and (4) explicit modeling of probabilities of binary drug-target interactions. Results: VB-MK-LMF achieves significantly better predictive performance in standard benchmarks compared to state-of-the-art methods, which can be traced back to multiple factors. The systematic evaluation of the effect of multiple kernels confirm their benefits, but also highlights the limitations of linear kernel combinations, already recognized in other fields. The analysis of the effect of prior kernels using varying sample sizes sheds light on the balance of data and knowledge in DTI tasks and on the rate at which the effect of priors vanishes. This also shows the existence of "small sample size" regions where using side information offers significant gains. Alongside favorable predictive performance, a notable property of MF methods is that they provide a unified space for drugs and targets using latent representations. Compared to earlier studies, the dimensionality of this space proved to be surprisingly low, which makes the latent representations constructed by VB-ML-LMF especially well-suited for visual analytics. The probabilistic nature of the predictions allows the calculation of the expected values of hits in functionally relevant sets, which we demonstrate by predicting drug promiscuity. The variational Bayesian approximation is also implemented for general purp

关键词： Drug-target interaction prediction Matrix factorization Multiple kernel learning Variational Bayes probabilistic graphical models

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Shape-Based Object Localization for Descriptive Classification

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2009年第1期84卷 40-62页

作者： Heitz, Geremy Elidan, Gal Packer, Benjamin Koller, Daphne Stanford Univ Stanford CA 94305 USA Hebrew Univ Jerusalem Dept Stat IL-91905 Jerusalem Israel

Discriminative tasks, including object categorization and detection, are central components of high-level computer vision. However, sometimes we are interested in a finer-grained characterization of the object's properties, such as its pose or articulation. In this paper we develop a probabilistic method (LOOPS) that can learn a shape and appearance model for a particular object class, and be used to consistently localize constituent elements (landmarks) of the object's outline in test images. This localization effectively projects the test image into an alternative representational space that makes it particularly easy to perform various descriptive tasks. We apply our method to a range of object classes in cluttered images and demonstrate its effectiveness in localizing objects and performing descriptive classification, descriptive ranking, and descriptive clustering.

关键词： probabilistic graphical models Deformable shape models Object recognition Markov random fields

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An interactive approach for Bayesian network learning using domain/expert knowledge

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2013年第8期54卷 1168-1181页

作者： Masegosa, Andres R. Moral, Serafin Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

Using domain/expert knowledge when learning Bayesian networks from data has been considered a promising idea since the very beginning of the field. However, in most of the previously proposed approaches, human experts do not play an active role in the learning process. Once their knowledge is elicited, they do not participate any more. The interactive approach for integrating domain/expert knowledge we propose in this work aims to be more efficient and effective. In contrast to previous approaches, our method performs an active interaction with the expert in order to guide the search based learning process. This method relies on identifying the edges of the graph structure which are more unreliable considering the information present in the learning data. Another contribution of our approach is the integration of domain/expert knowledge at different stages of the learning process of a Bayesian network: while learning the skeleton and when directing the edges of the directed acyclic graph structure. (C) 2013 Elsevier Inc. All rights reserved.

关键词： probabilistic graphical models Bayesian networks Interactive structure learning Domain expert knowledge Stochastic search

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A Bayesian stochastic search method for discovering Markov boundaries

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KNOWLEDGE-BASED SYSTEMS 2012年 35卷 211-223页

作者： Masegosa, Andres R. Moral, Serafin Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

The discovery of the Markov Boundary (MB) of a target variable using observational data plays a central role in feature selection and local causal structure inference. Most existing methods previously employed for this task rely on statistical independence tests and, in consequence, do not take into account the partial evidence that a finite data set gives about the existence of this kind of probabilistic relationships among random variables. In this work, we employ a novel stochastic search method which explicitly deals with this problem by eliciting multiple alternative Markov boundaries. This technique is based on a Bayesian approach for statistical tests and on a method to score the different alternative solutions. We have also evaluated an interactive procedure for integrating domain or expert knowledge a posteriori (after the learning process), in order to simplify and enrich the set of alternative inferred MBs. In an extensive experimental evaluation we show that this method is able to find a rich and accurate set of alternative MBs which, if properly combined, provide better inferences than other state-of-the-art approaches for this task. Moreover, we think that this new kind of methods, capable of capturing the inherent uncertainty of any real data set and which allows human interventions, can make practitioners feel more confident about the extracted knowledge than fully automatic approaches. (C) 2012 Elsevier B.V. All rights reserved.

关键词： probabilistic graphical models Markov boundaries Feature selection Bayesian methods Stochastic search

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