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33rd international Florida Artificial Intelligence Research Society conference, FLAIRS 2020

作者： Hartwig, Mattis Mohr, Marisa Moller, Ralf University of Lubeck Germany

ISBN: (纸本)9781577358213

probabilistic graphical models have been successfully applied in a lot of different fields, e.g., medical diagnosis and bio-statistics. Multiple specific extensions have been developed to handle, e.g., time-series data or Gaussian distributed random variables. In the case that handles both Gaussian variables and time-series data, downsides are that the models still have a discrete time-scale, evidence needs to be propagated through the graph and the conditional relationships between the variables are bound to be linear. This paper converts two probabilistic graphical models (the Markov chain and the hidden Markov model) into Gaussian processes by constructing covariance and mean functions, that encode the characteristics of the probabilistic graphical models. Our developed Gaussian process based formalism has the advantage of supporting a continuous time scale, direct inference from any time point to the other without propagation of evidence and flexibility to modify the covariance function if needed. © FLAIRS *** right reserved.

关键词： Hidden Markov models

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Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory Modelling 11

Bayesian Model Averaging of Chain Event Graphs for Robust Ex...

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international conference on probabilistic graphical models

作者： Strong, Peter Smith, Jim Q. Univ Warwick Ctr Complex Sci Coventry W Midlands England Univ Warwick Dept Stat Coventry W Midlands England Alan Turing Inst London England

Chain Event Graphs (CEGs) are a widely applicable class of probabilistic graphical model that can represent context-specific independence statements and asymmetric unfoldings of events in an easily interpretable way. Existing model selection literature on CEGs has largely focused on obtaining the maximum a posteriori (MAP) CEG. However, MAP selection is well-known to ignore model uncertainty. Here, we explore the use of Bayesian model averaging over this class. We demonstrate how this approach can quantify model uncertainty and leads to more robust inference by identifying shared features across multiple high-scoring models. Because the space of possible CEGs is huge, scoring models exhaustively for model averaging in all but small problems is prohibitive. However, we provide a simple modification of an existing model selection algorithm, that samples the model space, to illustrate the efficacy of Bayesian model averaging compared to more standard MAP modelling.

关键词： graphical models Explainable Modelling Bayesian Model Selection Chain Event Graphs Bayesian Model Averaging Robust Inference

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Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks 10

Two Reformulation Approaches to Maximum-A-Posteriori Inferen...

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10th international conference on probabilistic graphical models (PGM)

作者： Maua, Denis Deratani Ribeiro, Heitor Reis Katague, Gustavo Perez Antonucci, Alessandro Univ Sao Paulo Inst Math & Stat Sao Paulo Brazil Ist Dalle Molle Studi Intelligenza Artificiale Lugano Switzerland

Sum-product networks are expressive efficient probabilistic graphical models that allow for tractable marginal inference. Many tasks however require the computation of maximum-a-posteriori configurations, an NP-hard problem for such models. To date there have been very few proposals for computing maximum-a-posteriori configurations in sum-product networks. This is in sharp difference with other probabilistic frameworks such as Bayesian networks and random Markov fields, where the problem is also NP-hard. In this work we propose two approaches to reformulate maximuma-posteriori inference as other combinatorial optimization problems with widely available solvers. The first approach casts the problem as a similar inference problem in Bayesian networks, overcoming some limitations of previous similar translations. In addition to making available the toolset of maximum-a-posteriori inference on Bayesian networks to sum-product networks, our reformulation also provides further insight into the connections of these two classes of models. The second approach casts the problem as a mixed-integer linear program, for which there exists very efficient solvers. This allows such inferences to be enriched with integer-linear constraints, increasing the expressivity of the models. We compare our reformulation approaches in a large collection of problems, and against state-of-the-art approaches. The results show that reformulation approaches are competitive.

关键词： Tractable probabilistic models Sum-Product Networks probabilistic Circuits Maximuma-Posteriori Inference Mixed-Integer Linear Programming Bayesian Networks

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Bayesian Modelling of Student Misconceptions in the one-digit Multiplication with probabilistic Programming 16

Bayesian Modelling of Student Misconceptions in the one-digi...

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6th international conference on Learning Analytics and Knowledge (LAK)

作者： Taraghi, Behnam Saranti, Anna Legenstein, Robert Ebner, Martin Graz Univ Technol Educ Technol Munzgrabenstr 35 A-8010 Graz Austria Graz Univ Technol Inst Theoret Comp Sci Inffeldgasse 16b-1 A-8010 Graz Austria

ISBN: (纸本)9781450341905

One-digit multiplication errors are one of the most extensively analysed mathematical problems. Research work primarily emphasises the use of statistics whereas learning analytics can go one step further and use machine learning techniques to model simple learning misconceptions. probabilistic programming techniques ease the development of probabilistic graphical models (bayesian networks) and their use for prediction of student behaviour that can ultimately influence learning decision processes.

关键词： Learning Analytics Bayesian Modelling probabilistic Programming One-Digit Multiplication

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Solving Multiple Inference by Minimizing Expected Loss 10

Solving Multiple Inference by Minimizing Expected Loss

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10th international conference on probabilistic graphical models (PGM)

作者： Chen, Cong Yang, Jiaqi Chen, Chao Yuan, Changhe CUNY New York NY 10021 USA SUNY Stony Brook Stony Brook NY 11794 USA

Multiple Inference is the problem of finding multiple top solutions for an inference problem in a graphical model. It has been shown that it is beneficial for the top solutions to be diverse. However, existing methods, such as diverse M-Best and M-Modes, often rely on a hyper parameter in enforcing diversity. The optimal values of such parameters usually depend on the probability landscape of the graphical model and thus have to be tuned case by case via cross validation. This is not a desirable property. In this paper, we introduce a parameter-free method that directly minimizes the expected loss of each solution in finding multiple top solutions that have high oracle accuracy, and are automatically diverse. Empirical evaluations show that our method often have better performance than other competing methods.

关键词： graphical Model Multiple Inference Oracle Accuracy Expected Loss

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Causal data science for financial stress testing

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JOURNAL OF COMPUTATIONAL SCIENCE 2018年 26卷 294-304页

作者： Gao, Gelin Mishra, Bud Ramazzotti, Daniele NYU Courant Inst New York NY USA Stanford Univ Stanford CA 94305 USA

The most recent financial upheavals have cast doubt on the adequacy of some of the conventional quantitative risk management strategies, such as VaR (Value at Risk), in many common situations. Consequently, there has been an increasing need for verisimilar financial stress testings, namely simulating and analyzing financial portfolios in extreme, albeit rare scenarios. Unlike conventional risk management which exploits statistical correlations among financial instruments, here we focus our analysis on the notion of probabilistic causation, which is embodied by Suppes-Bayes Causal Networks (SBCNs);SBCNs are probabilistic graphical models that have many attractive features in terms of more accurate causal analysis for generating financial stress scenarios. In this paper, we present a novel approach for conducting stress testing of financial portfolios based on SBCNs in combination with classical machine learning classification tools. The resulting method is shown to be capable of correctly discovering the causal relationships among financial factors that affect the portfolios and thus, simulating stress testing scenarios with a higher accuracy and lower computational complexity than conventional Monte Carlo simulations. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Stress testing graphical models Causality Suppes-Bayes Causal Networks Classification Decision trees

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graphical models for Dependencies and Queries in Uncertain Data

Graphical Models for Dependencies and Queries in Uncertain D...

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26th IEEE international conference on Data Engineering (ICDE)

作者： Chen, Ruiwen Kiringa, Iluju Mao, Yongyi Univ Ottawa Ottawa ON Canada

ISBN: (纸本)9781424465217

Dependency and query are two fundamental concepts in databases. Specifically, hypergraph representations of join dependencies and conjunctive queries have been widely investigated for conventional relational databases. However, we still lack a systematic study of such graphical representations for uncertain and probabilistic databases. In this paper we initiate a comprehensive study of the role of graphical models in representing uncertainty and evaluating queries.

关键词： Graphic methods

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A probabilistic graphical model of quantum systems

A probabilistic graphical model of quantum systems

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9th international conference on Machine Learning and Applications, ICMLA 2010

作者： Yeang, Chen-Hsiang Institute of Statistical Science Academia Sinica Taipei Taiwan

ISBN: (纸本)9780769543000

Quantum systems are promising candidates of future computing and information processing devices. In a large system, information about the quantum states and processes may be incomplete and scattered. To integrate the distributed information we propose a quantum version of probabilistic graphical models. Variables in the model (quantum states and measurement outcomes) are linked by several types of operators (unitary, measurement, and merge/split operators). We propose algorithms for three machine learning tasks in quantum probabilistic graphical models: a belief propagation algorithm for inference of unknown states, an iterative algorithm for simultaneous estimation of parameter values and hidden states, and an active learning algorithm to select measurement operators based on observed evidence. We validate these algorithms on simulated data and point out future extensions toward a more comprehensive theory of quantum probabilistic graphical models. © 2010 IEEE.

关键词： Belief propagation

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Guided Open Story Generation Using probabilistic graphical models 19

Guided Open Story Generation Using Probabilistic Graphical M...

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14th international conference on the Foundations of Digital Games (FDG)

作者： Gandhi, Sagar Harrison, Brent Univ Kentucky Lexington KY 40506 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781450372176

In this work, we present an approach for performing computational storytelling in open domain based on Author Goals. Author Goals are constraints placed on a story event directed by the author of the system. There are two challenges present in this type of story generation: (1) automatically acquiring a model of story progression, and (2) guiding the progress of story progression in light of different goals. We propose a novel approach to story generation based on probabilistic graphical models and Loopy Belief Propagation (LBP) that addresses both of these problems. We show the applicability of our technique through a case study on the Visual Storytelling (VIST) 2017 dataset. We use image descriptions as author goals. This empirical analysis suggests that our approach is able to utilize goals information to better automatically generate stories.

关键词： Belief Propagation Computational Storytelling Natural Language Generation probabilistic graphical models

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Overlapping Communities and Roles in Networks with Node Attributes: probabilistic graphical Modeling, Bayesian Formulation and Variational Inference 31

Overlapping Communities and Roles in Networks with Node Attr...

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31st international Joint conference on Artificial Intelligence (IJCAI)

作者： Costa, Gianni Ortale, Riccardo ICAR CNR Arcavacata Di Rende Italy

ISBN: (纸本)9781956792003

We study the seamless integration of community discovery and behavioral role analysis, in the domain of networks with node attributes. In particular, we focus on unifying the two tasks, by explicitly harnessing node attributes and behavioral role patterns in a principled manner. To this end, we propose two Bayesian probabilistic generative models of networks, whose novelty consists in the interrelationship of overlapping communities, roles, their behavioral patterns and node attributes. The devised models allow for a variety of exploratory, descriptive and predictive tasks. These are carried out through mean-field variational inference, which is in turn mathematically derived and implemented into a coordinate-ascent algorithm. A wide spectrum of experiments is designed, to validate the devised models against three classes of state-of-the-art competitors using various real-world benchmark data sets from different social networking services.

关键词： Bayesian networks

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