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检索条件"任意字段=International Conference on Probabilistic Graphical Models"

共 872 条记录，以下是111-120 订阅

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probabilistic graphical models AS TOOLS FOR EVALUATING THE IMPACT OF USAGE-CONTEXT ON THE ENVIRONMENTAL PERFORMANCE OF PRODUCTS

PROBABILISTIC GRAPHICAL MODELS AS TOOLS FOR EVALUATING THE I...

引用

ASME international Design Engineering Technical conferences/Computers Information in Engineering conference

作者： Telenko, Cassandra Seepersad, Carolyn Univ Texas Austin Dept Mech Engn Austin TX 78712 USA

ISBN: (纸本)9780791845042

Environmentally conscious design is focused on reducing the environmental impact of engineered systems, but common practice in life cycle analysis overlooks the relationship between a product's usage-context and its environmental performance. Existing studies rarely consider operational variability or the correlation between performance, design, and usage variables. probabilistic graphical models (PGMs) provide the capability of not only evaluating uncertainty and variability of product use, but also correlating the results with the product's features and usage context. This discussion explores the use of PGMs as a tool for evaluating operational variability in products and including the results in life cycle inventories. The tool is illustrated for environmentally conscious product design through an example study of an electric kettle.

关键词： Product design

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Background and perspectives of possibilistic graphical models 1st

Background and perspectives of possibilistic graphical model...

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1st international Joint conference on Qualitative and Quantitative Practical Reasoning (ECSQARU-FAPR 97)

作者： Gebhardt, J Kruse, R Tech Univ Carolo Wilhelmina Braunschweig Dept Math & Comp Sci D-38106 Braunschweig Germany Otto Von Guericke Univ Dept Comp Sci D-39106 Magdeburg Germany

ISBN: (纸本)3540630953

graphical modelling is an important tool for the efficient representation and analysis of uncertain information in knowledge-based systems. While Bayesian networks and Markov networks from probabilistic graphical modelling are well-known for a couple of years, the field of possibilistic graphical modelling occurs as a new promising area of research. Possibilistic networks provide an alternative approach compared to probabilistic networks, whenever it is necessary to model uncertainty and imprecision as two different kinds of imperfect information. Imprecision in the sense of set-valued data has often to be considered in situations where data are obtained from human observations or non-precise measurement units. In this contribution we present a comparison of the background and perspectives of probabilistic and possibilistic graphical models, and give an overview on the current state of the art of possibilistic networks with respect to propagation and learning algorithms, applicable to data mining and data fusion problems.

关键词： Knowledge based systems

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Bean Machine: A Declarative probabilistic Programming Language For Efficient Programmable Inference 10

Bean Machine: A Declarative Probabilistic Programming Langua...

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10th international conference on probabilistic graphical models (PGM)

作者： Tehrani, Nazanin Arora, Nimar S. Li, Yucen Lily Shah, Kinjal Divesh Noursi, David Tingley, Michael Torabi, Narjes Masouleh, Sepehr Lippert, Eric Meijer, Erik Facebook Inc Menlo Pk CA 94025 USA

A number of imperative probabilistic Programming Languages (PPLs) have been recently proposed, but the imperative style choice makes it very hard to deduce the dependence structure between the latent variables, which can also change from iteration to iteration. We propose a new declarative style PPL, Bean Machine, and demonstrate that in this new language, the dynamic dependence structure is readily available. Although we are not the first to propose a declarative PPL or to observe the advantages of knowing the dependence structure, we take the idea further by showing other inference techniques that become feasible or easier in this style. We show that it is very easy for users to program inference by composition (combining different inference techniques for different parts of the model), customization (providing a custom hand-written inference method for specific variables), and blocking (specifying blocks of random variables that should be sampled together) in a declarative language. A number of empirical results are provided where we backup these claims modulo the runtime inefficiencies of unvectorized Python. As a fringe benefit, we note that it is very easy to translate statistical models written in mathematical notation into our language.

关键词： probabilistic Programming Programmable Inference Declarative Structure

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The Dual PC Algorithm for Structure Learning 11

The Dual PC Algorithm for Structure Learning

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international conference on probabilistic graphical models

作者： Giudice, Enrico Kuipers, Jack Moffa, Giusi Univ Basel Dep Math & Comp Sci Basel Switzerland Swiss Fed Inst Technol Dep Biosyst Sci & Engn Basel Switzerland UCL Div Psychiat London England

Learning the graphical structure of Bayesian networks is key to describing data generating mechanisms in many complex applications and it poses considerable computational challenges. Observational data can only identify the equivalence class of the directed acyclic graph underlying a Bayesian network model, and a variety of methods exist to tackle the problem. Under certain assumptions, the popular PC algorithm can consistently recover the correct equivalence class by reverse-engineering the conditional independence (CI) relationships holding in the variable distribution. Here, we propose the dual PC algorithm, a novel scheme to carry out the CI tests within the PC algorithm by leveraging the inverse relationship between covariance and precision matrices. By exploiting block matrix inversions we can efficiently supplement partial correlation tests at each step with those of complementary (or dual) conditioning sets. The multiple CI tests of the dual PC algorithm proceed by first considering marginal and full-order CI relationships and progressively moving to central-order ones. Simulation studies show that the dual PC algorithm outperforms the classic PC algorithm both in terms of run time and in recovering the underlying network structure, even in the presence of deviations from Gaussianity.

关键词： Bayesian Networks graphical models Directed Acyclic Graphs Structure Learning PC Algorithm

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Structured Factored Inference for probabilistic Programming 21

Structured Factored Inference for Probabilistic Programming

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21st international conference on Artificial Intelligence and Statistics (AISTATS)

作者： Pfeffer, Avi Ruttenberg, Brian Kretschmer, William O'Connor, Alison Charles River Analyt Cambridge MA 02138 USA MIT Cambridge MA 02139 USA

probabilistic reasoning on complex real-world models is computationally challenging. Inference algorithms have been developed that work well on specific models or on parts of general models, but they require significant hand-engineering to apply to full-scale problems. probabilistic programming (PP) enables the expression of rich probabilistic models, but inference remains a bottleneck in many applications. Factored inference is one of the main approaches to inference in graphical models, but has trouble scaling up to some hard problems expressible as probabilistic programs. We present structured factored inference (SFI), a framework that enables factored inference algorithms to scale to significantly more complex programs. Using models encoded in a PP language, SFI provides a sound means to decompose a model into submodels, apply an algorithm to each submodel, and combine results to answer a query. Our results show that SFI successfully reasons on models where standard factored inference algorithms fail due to computational complexity. SFI is nearly as accurate as exact inference and is as fast as approximate inference methods.

关键词： Query processing

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Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic graphical models 12

Kernel-Based Differentiable Learning of Non-Parametric Direc...

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12th international conference on probabilistic graphical models (PGM)

作者： Liang, Yurou Zadorozhnyi, Oleksandr Drton, Mathias Tech Univ Munich Munich Germany Munich Ctr Machine Learning Munich Germany

Causal discovery amounts to learning a directed acyclic graph (DAG) that encodes a causal model. This model selection problem can be challenging due to its large combinatorial search space, particularly when dealing with non-parametric causal models. Recent research has sought to bypass the combinatorial search by reformulating causal discovery as a continuous optimization problem, employing constraints that ensure the acyclicity of the graph. In non-parametric settings, existing approaches typically rely on finite-dimensional approximations of the relationships between nodes, resulting in a score-based continuous optimization problem with a smooth acyclicity constraint. In this work, we develop an alternative approximation method by utilizing reproducing kernel Hilbert spaces (RKHS) and applying general sparsity-inducing regularization terms based on partial derivatives. Within this framework, we introduce an extended RKHS representer theorem. To enforce acyclicity, we advocate the log-determinant formulation of the acyclicity constraint and show its stability. Finally, we assess the performance of our proposed RKHS-DAGMA procedure through simulations and illustrative data analyses.

关键词： Causal discovery graphical model kernel methods RKHS structural equation model

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Bayesian network structure learning with causal effects in the presence of latent variables 10

Bayesian network structure learning with causal effects in t...

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10th international conference on probabilistic graphical models (PGM)

作者： Chobtham, Kiattikun Constantinou, Anthony C. Queen Mary Univ London Bayesian Artificial Intelligence Res Lab Risk & Informat Management Res Grp Sch Elect Engn & Comp Sci London England

Latent variables may lead to spurious relationships that can be misinterpreted as causal relationships. In Bayesian Networks (BNs), this challenge is known as learning under causal insufficiency. Structure learning algorithms that assume causal insufficiency tend to reconstruct the ancestral graph of a BN, where bi-directed edges represent confounding and directed edges represent direct or ancestral relationships. This paper describes a hybrid structure learning algorithm, called CCHM, which combines the constraint-based part of cFCI with hill-climbing score-based learning. The score-based process incorporates Pearl's do-calculus to measure causal effects, which are used to orientate edges that would otherwise remain undirected, under the assumption the BN is a linear Structure Equation Model where data follow a multivariate Gaussian distribution. Experiments based on both randomised and well-known networks show that CCHM improves the state-of-the-art in terms of reconstructing the true ancestral graph.

关键词： ancestral graphs causal discovery causal insufficiency probabilistic graphical models

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GRN model of probabilistic databases: Construction, transition and querying 10

GRN model of probabilistic databases: Construction, transiti...

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2010 international conference on Management of Data, SIGMOD '10

作者： Chen, Ruiwen Mao, Yongyi Kiringa, Iluju University of Ottawa Canada

ISBN: (纸本)9781450300322

Under the tuple-level uncertainty paradigm, we formalize the use of a novel graphical model, Generator-Recognizer Network (GRN), as a model of probabilistic databases. The GRN modeling framework is capable of representing a much wider range of tuple dependency structure. We show that a GRN representation of a probabilistic database may undergo transitions induced by imposing constraints or evaluating queries. We formalize procedures for these two types of transitions such that the resulting graphical models after transitions remain as GRNs. This formalism makes GRN a self-contained modeling framework and a closed representation system for probabilistic databases - a property that is lacking in most existing models. In addition, we show that exploiting the transitional mechanisms allows a systematic approach to constructing GRNs for arbitrary probabilistic data at arbitrary stages. Advantages of GRNs in query evaluation are also demonstrated. © 2010 ACM.

关键词： Bayesian networks

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A GENERAL BAYESIAN ALGORITHM FOR VISUAL OBJECT TRACKING BASED ON SPARSE FEATURES

A GENERAL BAYESIAN ALGORITHM FOR VISUAL OBJECT TRACKING BASE...

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IEEE international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Soto, Mauricio Regazzoni, Carlo S. Univ Genoa Dept Biophys & Elect Engn Genoa Italy

ISBN: (纸本)9781457705397

This paper describes a Bayesian algorithm for rigid/non-rigid 2D visual object tracking based on sparse image features. The algorithm is inspired by the way human visual cortex segments and tracks different moving objects within its FOV by constructing dynamical nonretinotopic layers. The method is explained as a recursive algorithm between time slices (intra-slice) and as a forward-backward message passing within every time slice (inter-slice) under the probabilistic graphical Model (PGM) framework. Finally, an observation model function that resembles the Generalized Hough Transform and allows exploiting internal structure of the problem is employed in order to increase the robustness and accuracy of the algorithm against clutter and missed detections.

关键词： Visual Object Tracking probabilistic graphical models Nonretinotopic Representation Generalized Hough Transform

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MAsCOT: Self-adaptive Opportunistic Offloading for Cloud-Enabled Smart Mobile Applications with probabilistic graphical models at Runtime 49

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

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49th Hawaii international conference on System Sciences (HICSS)

作者： Naqvi, Nayyab Zia Devlieghere, Jonas Preuveneers, Davy Berbers, Yolande Katholieke Univ Leuven iMinds DistriNet Leuven Belgium

ISBN: (纸本)9780769556703

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

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