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international Joint conference on Neural Networks (IJCNN)

作者： Florencio, Heitor Neto, Adriao Doria Fed Univ Rio Grande Norte UFRN Dept Automat & Comp Engn Natal RN Brazil

ISBN: (纸本)9781509060146

The combined use of graphical models and probabilistic techniques has been shown to be highly effective in applications involving the uncertain data from industrial environments. In addition, industrial data analysis has been a necessary and interesting approach for systems in industry 4.0. The present paper proposes a probabilistic graphical model to infer the probability of the path presenting a packet delivery ratio (PDR) above a threshold. The technique used was Bayesian Network, however selection and discretization techniques were applied prior to data processing. These data were collected froma real WirelessHART network. The results show the applicability of the probabilistics method to real problems of industrial network.

关键词： probabilistic graphical models Indutrial Wireless Sensor Network Bayesian models WirelessHART Network

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On generating random Gaussian graphical models

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international JOURNAL OF APPROXIMATE REASONING 2020年 125卷 240-250页

作者： Cordoba, Irene Varando, Gherardo Bielza, Concha Larranaga, Pedro Univ Politecn Madrid Madrid Spain Univ Copenhagen Copenhagen Denmark

Structure learning methods for covariance and concentration graphs are often validated on synthetic models, usually obtained by randomly generating: (i) an undirected graph, and (ii) a compatible symmetric positive definite (SPD) matrix. In order to ensure positive definiteness in (ii), a dominant diagonal is usually imposed. In this work we investigate different methods to generate random symmetric positive definite matrices with undirected graphical constraints. We show that if the graph is chordal it is possible to sample uniformly from the set of correlation matrices compatible with the graph, while for general undirected graphs we rely on a partial orthogonalization method. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Concentration graph Covariance graph Positive definite matrix simulation Undirected graphical model Algorithm validation

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Learning directed acyclic graph SPNs in sub-quadratic time

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international JOURNAL OF APPROXIMATE REASONING 2020年 120卷 48-73页

作者： Ghose, Amur Jaini, Priyank Poupart, Pascal Univ Waterloo Waterloo ON Canada Vector Inst Toronto ON Canada

In this paper, we present Prometheus, a graph partitioning based algorithm that creates multiple variable decompositions efficiently for learning Sum-Product Network structures across both continuous and discrete domains. Prometheus proceeds by creating multiple candidate decompositions that are represented compactly with an acyclic directed graph in which common parts of different decompositions are shared. It eliminates the correlation threshold hyperparameter often used in other structure learning techniques, allowing Prometheus to learn structures that are robust in low data regimes. Prometheus outperforms other structure learning techniques in 30 discrete and continuous domains. We also extend Prometheus to exploit sparsity in correlations between features in order to obtain an efficient sub-quadratic algorithm (w.r.t. the number of features) that scales better to high dimensional datasets. (C) 2020 The Authors. Published by Elsevier Inc.

关键词： Sum-product networks probabilistic graphical models Directed acyclic graph Structure learning Bayesian networks

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Evaluating probabilistic graphical models for forecasting 18

Evaluating probabilistic graphical models for forecasting

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2015 18th international conference on Intelligent System Application to Power Systems (ISAP)

作者： Ibarguengoytia, Pablo H. Reyes, Alberto Garcia, Uriel A. Romero, Ines Pech, David Inst Invest Elect Av Reforma 113 Cuernavaca 62490 Morelos Mexico

ISBN: (纸本)9781509001910

Forecasting represents a very important task for control and decision making in many fields. Forecasting the dollar price is important for global companies to plan their investments. Forecasting the wind speed for a day-ahead horizon allows dispatching clean energy efficiently. One technique developed by the artificial intelligence community that has proved to be efficient for forecasting is the probabilistic graphical models approach. In order to obtain accurate models for forecasting, there exist different assumptions that might be made. This paper presents these assumptions and the results of different experiments conducted to define the characteristics of good probabilistic graphical models. A performance comparison of several graphical models is also presented. The experiments were executed to forecast wind velocity and hence, wind power in wind farms.

关键词： Forecasting probabilistic graphical models Renewable energy

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Unifying learning in games and graphical models

Unifying learning in games and graphical models

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7th international conference on Information Fusion (FUSION)

作者： Rezek, I Roberts, SJ Rogers, A Dash, RK Jennings, N Univ Oxford Dept Engn Sci Oxford OX1 3PJ England

ISBN: (纸本)0780392868

The ever increasing use of intelligent multi-agent systems poses increasing demands upon them. One of these is the ability to reason consistently under uncertainty. This, in turn, is the dominant characteristic of probabilistic learning in graphical models which, however, lack a natural decentralised formulation. The ideal would, therefore, be a unifying framework which is able to combine the strengths of both multi-agent and probabilistic inference In this paper we present a unified interpretation of the inference mechanisms in games and graphical models. In particular, we view fictitious play as a method of optimising the Kullback-Leibler distance between current mixed strategies and optimal mixed strategies at Nash equilibrium. In reverse, probabilistic inference in the variational mean-field framework can be viewed as fictitious game play to learn the best strategies which explain a probabilistic graphical model.

关键词： Learning systems

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Online Single-Microphone Source Separation using Non-Linear Autoregressive models 11

Online Single-Microphone Source Separation using Non-Linear ...

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international conference on probabilistic graphical models

作者： van Erp, Bart de Vries, Bert Eindhoven Univ Technol Eindhoven Netherlands GN Hearing Eindhoven Netherlands

In this paper a modular approach to single-microphone source separation is proposed. A probabilistic model for mixtures of observations is constructed, where the independent underlying source signals are described by non-linear autoregressive models. Source separation in this model is achieved by performing online probabilistic inference through an efficient message passing procedure. For retaining tractability with the non-linear autoregressive models, three different approximation methods are described. A set of experiments shows the effectiveness of the proposed source separation approach. The source separation performance of the different approximation methods is quantified through a set of verification experiments. Our approach is validated in a speech denoising task.

关键词： Kalman filtering Message passing Non-linear autoregressive models probabilistic inference Source separation

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Towards Model-Driven Engineering for Big Data Analytics - An Exploratory Analysis of Domain-Specific Languages for Machine Learning

Towards Model-Driven Engineering for Big Data Analytics - An...

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47th Annual Hawaii international conference on System Sciences

作者： Breuker, Dominic Univ Muenster ERCIS Munster Germany

ISBN: (纸本)9781479925049

graphical models and general purpose inference algorithms are powerful tools for moving from imperative towards declarative specification of machine learning problems. Although graphical models define the principle information necessary to adapt inference algorithms to specific probabilistic models, entirely model-driven development is not yet possible. However, generating executable code from graphical models could have several advantages. It could reduce the skills necessary to implement probabilistic models and may speed up development processes. Both advantages address pressing industry needs. They come along with increased supply of data scientist labor, the demand of which cannot be fulfilled at the moment. To explore the opportunities of model-driven big data analytics, I review the main modeling languages used in machine learning as well as inference algorithms and corresponding software implementations. Gaps hampering direct code generation from graphical models are identified and closed by proposing an initial conceptualization of a domain-specific modeling language.

关键词： Big Data computer graphics data analysis inference mechanisms learning (artificial intelligence) program compilers specification languages big data analytics direct code generation domain-specific languages domain-specific modeling language general purpose inference algorithms graphical models machine learning problems model-driven development model-driven engineering modeling languages probabilistic models Adaptation models Computational modeling Data models graphical models Inference algorithms Random variables Unified modeling language graphical models Machine Learning Model-driven Engineering

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probabilistic graphical models with Neural Networks in InferPy 10

Probabilistic Graphical Models with Neural Networks in Infer...

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10th international conference on probabilistic graphical models (PGM)

作者： Cabanas, Rafael Cozar, Javier Salmeron, Antonio Masegosa, Andres R. Ist Dalle Molle Studi Intelligenza Artificiale ID Lugano Switzerland Univ Almeria Dept Math Almeria Spain Univ Almeria Ctr Dev & Transfer Math Res Ind CDTIME Almeria Spain

InferPy is an open-source Python package for variational inference in probabilistic models containing neural networks. Other similar libraries are often difficult for non-expert users. InferPy provides a much more compact and simple way to code such models, at the expense of slightly reducing expressibility and flexibility. The main objective of this package is to permit its use without having a strong theoretical background or thorough knowledge of the deep learning frameworks.

关键词： Deep probabilistic modeling Hierarchical probabilistic models Variational Inference Bayesian learning TensorFlow Keras User-friendly

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Highly Efficient Structural Learning of Sparse Staged Trees 11

Highly Efficient Structural Learning of Sparse Staged Trees

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international conference on probabilistic graphical models

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

Several structural learning algorithms for staged tree models, an asymmetric extension of Bayesian networks, have been defined. However, they do not scale efficiently as the number of variables considered increases. Here we introduce the first scalable structural learning algorithm for staged trees, which searches over a space of models where only a small number of dependencies can be imposed. A simulation study as well as a real-world application illustrate our routines and the practical use of such data-learned staged trees.

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

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Robust Estimation of Laplacian Constrained Gaussian graphical models with Trimmed Non-convex Regularization 11

Robust Estimation of Laplacian Constrained Gaussian Graphica...

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international conference on probabilistic graphical models

作者： Vieyra, Mariana Vargas Inria Grenoble Grenoble France

The problem of discovering a structure that fits a collection of vector data is of crucial importance for a variety of applications. Such problems can be framed as Laplacian constrained Gaussian graphical Model inference. Existing algorithms rely on the assumption that all the available observations are drawn from the same Multivariate Gaussian distribution. However, in practice it is common to find scenarios where the datasets are contaminated with a certain number of outliers. The purpose of this work is to address that problem. We propose a robust method based on Trimmed Least Squares that copes with the presence of corrupted samples. We provide statistical guarantees on the estimation error and present results on both simulated data and real-world data.

关键词： Laplacian-constrained Gaussain graphical models graph learning

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