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Sensitivity Statistical Estimates for Local A Posteriori Inference Matrix-Vector Equations in Algebraic Bayesian Networks over Quantum Propositions

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VESTNIK ST PETERSBURG UNIVERSITY-MATHEMATICS 2018年第1期51卷 42-48页

作者： Zolotin, A. A. Tulupyev, A. L. St Petersburg State Univ Univ Skaya Nab 7-9 St Petersburg 199034 Russia Russian Acad Sci St Petersburg Inst Informat & Automat 14 Ya Liniya VO 39 St Petersburg 199178 Russia

An approach to the sensitivity analysis of local a posteriori inference equations in algebraic Bayesian networks is proposed in this paper. Some basic definitions and formulations are briefly given and the development of the matrix-vector a posteriori inference approach is considered. Some cases of the propagation of deterministic and stochastic evidence in a knowledge pattern with scalar estimates of component truth probabilities over quantum propositions are described. For each of the considered cases, the necessary metrics are introduced, and some transformations resulting in four linear programming problems are performed. The solution of these problems gives the required estimates. In addition, two theorems postulating the covering estimates for the considered parameters are formulated. The results obtained in this work prove the correct application of models and create a basis for the sensitivity analysis of local and global probabilistic-logic inference equations.

关键词： uncertain knowledge propagation of evidence probabilistic logic algebraic Bayesian networks probabilistic-logic inference sensitivity statistical estimates probabilistic graphical models matrix-vector equations

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A Fully Polynomial Time Approximation Scheme for Updating Credal Networks of Bounded Treewidth and Number of Variable States

A Fully Polynomial Time Approximation Scheme for Updating Cr...

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7th International Symposium on Imprecise Probability - Theories and Applications (ISIPTA)

作者： Maua, Denis D. de Campos, Cassio P. Zaffalon, Marco IDSIA Galleria 2 CH-6928 Manno Switzerland

ISBN: (纸本)9783902652409

Credal networks lift the precise probability assumption of Bayesian networks, enabling a richer representation of uncertainty in the form of closed convex sets of probability measures. The increase in expressiveness comes at the expense of higher computational costs. In this paper we present a new algorithm which is an extension of the well-known variable elimination algorithm for computing posterior inferences in extensively specified credal networks. The algorithm efficiency is empirically shown to outperform a state-of-the-art algorithm. We then provide the first fully polynomial time approximation scheme for inference in credal networks with bounded treewidth and number of states per variable.

关键词： probabilistic graphical models credal networks approximation scheme valuation algebra

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Comparison of Two graphical models Approaches for the modelling of Multi-components System's Reliability

Comparison of Two Graphical Models Approaches for the modell...

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International Conference on Computers and Industrial Engineering (CIE39)

作者： Donat, Roland Bouillaut, Laurent Neji, Abdelmoez Aknin, Patrice French Natl Inst Transportat & Safety Res F-93166 Noisy Le Grand France

ISBN: (纸本)9781424441358

Reliability analysis is an integral part of system design and operating. Moreover, it can be an input to optimize maintenance policies. Recently, Bayesian Networks (BN) and Dynamic Bayesian Networks (DBN) have been proved relevant to represent complex systems and perform reliability studies. The major drawback of this approach comes from the constraint on the state sojourn times which are necessarily exponentially distributed, as in usual markovian approaches. Therefore, a new formalism was previously introduced in the literature to avoid this constraint: the graphical Duration models (GDM). This paper aims to quantify the reliability estimation error due to an exponential approximation when the system follows other kinds of sojourn time's distributions. Finally results obtained by DBN and GDM will be compared.

关键词： Reliability Markov models Semi-Markov process probabilistic graphical models Bayesian Network Maintenance

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Multi-Label Truth Inference for Crowdsourcing Using Mixture models

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IEEE Transactions on Knowledge and Data Engineering 2021年第5期33卷 2083-2095页

作者： Zhang, Jing Wu, Xindong School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei 230009 China

When acquiring labels from crowdsourcing platforms, a task may be designed to include multiple labels and the values of each label may belong to a set of various distinct options, which is the so-called multi-class multi-label annotation. To improve the quality of labels, requesters usually let one task be independently completed by a group of heterogeneous crowdsourced workers. Then, the true values of the multiple labels of each task are inferred from these repeated noisy labels. In this paper, we propose two novel probabilistic models MCMLI and MCMLD to address the multi-class multi-label inference problem in crowdsourcing. MCMLI assumes that the labels of each task are mutually independent and MCMLD utilizes a mixture of multiple independently multinoulli distributions to capture the correlation among the labels. Both models can jointly infer multiple true labels of each instance as well as estimate the reliability of crowdsourced workers modeled by a set of confusion matrices with an expectation-maximization algorithm. Experiments with three typical crowdsourcing scenarios and a real-world dataset show that our proposed models significantly outperform existing competitive alternatives. When the labels are strongly correlated, MCMLD substantially outperforms MCMLI. Furthermore, our models can be easily simplified to the one-coin models, which show more advantageous when errors are uniformly distributed, or labels are sparse. © 1989-2012 IEEE.

关键词： Crowdsourcing maximum likelihood estimation mixture models probabilistic graphical models statistical inference

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AXON SEGMENTATION IN MICROSCOPY IMAGES - A graphical MODEL BASED APPROACH

AXON SEGMENTATION IN MICROSCOPY IMAGES - A GRAPHICAL MODEL B...

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9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Golabchi, F. Noushin Brooks, Dana H. Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

ISBN: (纸本)9781457718588

Image segmentation of very large and complex microscopy images are challenging due to variability in the images and the need for algorithms to be robust, fast and able to incorporate various types of information and constraints in the segmentation model. In this paper we propose a graphical model based image segmentation framework that combines the information in images regions with the information in their boundary in a unified probabilistic formulation.

关键词： model based image segmentation probabilistic graphical models microscopy image segmentation axon segmentation

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Soft Learning probabilistic Circuits 12

Soft Learning Probabilistic Circuits

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12th International Conference on probabilistic graphical models (PGM)

作者： Ghandi, Soroush Quost, Benjamin de Campos, Cassio Eindhoven Univ Technol Eindhoven Netherlands Univ Technol Compiegne Compiegne France

probabilistic Circuits (PCs) are prominent tractable probabilistic models, allowing for a wide range of exact inferences. This paper focuses on the main algorithm for training PCs, LearnSPN, a gold standard due to its efficiency, performance, and ease of use, in particular for tabular data. We show that LearnSPN is a greedy likelihood maximizer under mild assumptions. While inferences in PCs may use the entire circuit structure for processing queries, LearnSPN applies a hard method for learning them, propagating at each sum node a data point through one and only one of the children/edges as in a hard clustering process. We propose a new learning procedure named SoftLearn, that induces a PC using a soft clustering process. We investigate the effect of this learning-inference compatibility in PCs. Our experiments show that SoftLearn outperforms LearnSPN in many situations, yielding better likelihoods and arguably better samples. We also analyze comparable tractable models to highlight the differences between soft/hard learning and model querying.

关键词： probabilistic circuits probabilistic inference probabilistic graphical models

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Agent-based Knowledge Evolution Management and Fuzzy Rule-based Evolution Detection in Bayesian Networks

Agent-based Knowledge Evolution Management and Fuzzy Rule-ba...

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International Conference on Fuzzy Theory and Its Applications (iFUZZY)

作者： Lee, Kyung Mi Lee, Keon Myung Chungbuk Natl Univ Dept Comp Sci Cheongju Chungbuk South Korea

ISBN: (纸本)9781479903863

All data and information are not always available at the time of a system design and implementation. Especially in knowledge-based systems, training data could be limited at the early stage and more training data might be acquired after the system deployment. This paper is concerned with a method to keep track of knowledge evolution and to detect the changes in the knowledge as more training data are provided. The method assumes that the knowledge is expressed in Bayesian networks and makes use of an agent framework for autonomous processing of knowledge evolution and change detection. It maintains sufficient statistics using a tiled sliding window structure. In order to flexibly encode the strategy for detecting the changes in the joint probability distributions, a set of fuzzy rules are used with which application domains specify their own strategy.

关键词： Knowledge evolution Bayesian network Agent systems fuzzy inference probabilistic graphical models

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Facial Expression Recognition with Discriminatory graphical models 2

Facial Expression Recognition with Discriminatory Graphical ...

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2nd International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Hasani, Behzad Arzani, Mohammad M. Fathy, Mahmood Raahemifar, Kaamran Iran Univ Sci & Technol Dept Comp Engn Tehran Iran Ryerson Univ Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9781509058204

Discriminating probabilistic graphical models are reliable tools for a sequence labeling task. Conditional Random Fields (CRFs) are discriminative models which will enable us to label a sequence of input data. Other variations of CRFs have been proposed. Hidden Conditional Random Fields (HCRFs) incorporate hidden states to the CRF model and assign a label for the whole input sequence as the model's output. Latent-Dynamic Conditional Random Fields (LDCRFs) also incorporate hidden variable states to the CRFs, in addition, these models are able to label each output variables separately. These models can capture subtle changes among different classes which will help us to achieve better recognition results. In this work we experiment various models and settings in order to achieve better results in facial expression recognition from sequence of videos. We use CRF and LDCRF models and train them with Limited-memory BFGS and Conjugate Gradient parameter learning methods. For each model we use various feature vectors in order to achieve better recognition results. We use Active Appearance Model (AAM) landmark points, Histogram of Oriented Gradients (HOG) and Uniform Local Binary Pattern (U-LBP) as our feature vectors in our models. We show which combination of learning methods and feature vectors are suitable for CRF and LDCRF discriminative models.

关键词： probabilistic graphical models Discriminative models Pattern Local Binary Active Appearance models Facial Expression

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Neural Graph Revealers 1st

Neural Graph Revealers

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1st International Workshop on Machine Learning for Multimodal Healthcare Data (ML4MHD)

作者： Shrivastava, Harsh Chajewska, Urszula Microsoft Res Redmond WA 98052 USA

ISBN: (纸本)9783031476785;9783031476792

Sparse graph recovery methods work well where the data follows their assumptions, however, they are not always designed for doing downstream probabilistic queries. This limits their adoption to only identifying connections among domain variables. On the other hand, probabilistic graphical models (PGMs) learn an underlying base graph together with a distribution over the variables (nodes). PGM design choices are carefully made such that the inference and sampling algorithms are efficient. This results in certain restrictions and simplifying assumptions. In this work, we propose Neural Graph Revealers (NGRs) which attempt to efficiently merge the sparse graph recovery methods with PGMs into a single flow. The task is to recover a sparse graph showing connections between the features and learn a probability distribution over them at the same time. NGRs use a neural network as a multi-task learning framework. We introduce graph-constrained path norm that NGRs leverage to learn a graphical model that captures complex non-linear functional dependencies between features in the form of an undirected sparse graph. NGRs can handle multimodal inputs like images, text, categorical data, embeddings etc. which are not straightforward to incorporate in the existing methods. We show experimental results on data from Gaussian graphical models and a multimodal infant mortality dataset by CDC (Software: https://***/harshs27/neural-graph-revealers).

关键词： Sparse Graph Recovery probabilistic graphical models Deep Learning

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Insights from the Long-Tail : Learning Latent Representations of Online User Behavior in the Presence of Skew and Sparsity 18

Insights from the Long-Tail : Learning Latent Representation...

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27th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Krishnan, Adit Sharma, Ashish Sundaram, Hari Univ Illinois Champaign IL 61820 USA Microsoft Res Bengaluru India

ISBN: (纸本)9781450360142

This paper proposes an approach to learn robust behavior representations in online platforms by addressing the challenges of user behavior skew and sparse participation. Latent behavior models are important in a wide variety of applications: recommender systems;prediction;user profiling;community characterization. Our framework is the first to jointly address skew and sparsity across graphical behavior models. We propose a generalizable bayesian approach to partition users in the presence of skew while simultaneously learning latent behavior profiles over these partitions to address user-level sparsity. Our behavior profiles incorporate the temporal activity and links between participants, although the proposed framework is flexible to introduce other definitions of participant behavior. Our approach explicitly discounts frequent behaviors and learns variable size partitions capturing diverse behavior trends. The partitioning approach is data-driven with no rigid assumptions, adapting to varying degrees of skew and sparsity. A qualitative analysis indicates our ability to discover niche and informative user groups on large online platforms. Results on User Characterization (+6-22% AUC);Content Recommendation (+6-43% AUC) and Future Activity Prediction (+12-25% RMSE) indicate significant gains over state-of-the-art baselines. Furthermore, user cluster quality is validated with magnified gains in the characterization of users with sparse activity.

关键词： Behavior Analysis probabilistic graphical models Behavior Skew Data Sparsity Interactive Media Platforms

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