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检索条件"主题词=Inference algorithms"

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inference algorithms for Probabilistic Models with Applications in Epidemiology and Space Weather Forecasting

Inference Algorithms for Probabilistic Models with Applicati...

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作者： Iong, Daniel University of Michigan

学位级别：Ph.D., Doctor of Philosophy

Probabilistic models are able to encode our beliefs about underlying data generating mechanisms through distributional assumptions about model parameters and latent, or hidden, variables. Probabilistic models have had a huge impact on a wide range of fields such as epidemiology, biology, astronomy, psychology and much more. Due to their ability to encode complex structures, performing inference with these models can be challenging. This thesis proposes several inference algorithms for probabilistic models that are used to analyze data in epidemiology and space weather forecasting. In the second chapter, we propose a Monte-Carlo Expectation-Maximization (EM) algorithm for a novel mixture model used to model heterogeneous causal mechanisms in Mendelian Randomization (MR). We also derive approximate confidence intervals for uncertainty quantification, and adopt a modified Bayesian information criterion (BIC) for model selection. Our proposed model clusters the effects in a linear error-in-variables regression model using a Gaussian mixture model. We verify the efficacy of the Monte-Carlo EM algorithm, approximate confidence intervals, and model selection criterion through extensive simulation studies. We show that it is able to identify heterogeneous causal effects in MR by using it to study the causal relationship between high-density lipoprotein (HDL) cholesterol and coronary heart disease. In the third chapter, we introduce a variational inference algorithm for fitting robust Gaussian process regression (GPR) models with contaminated normal noise. Most existing robust GPR methods cannot be applied to large datasets as they perform exact inference which scales poorly with number of observations. We approximate the GPR model with contaminated normal noise with a sparse variational approximation and develop a generalized alternating minimization (GAM) algorithm for jointly estimating the variational and model parameters. Through simulation studies, we show that our pro

关键词： Applied statistics inference algorithms Probabilistic models Weather forecasting Space weather

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inference algorithms for the Multiplicative Mixture Mallows Model

Inference Algorithms for the Multiplicative Mixture Mallows ...

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International Conference on Signal Processing and Communications, SPCOM

作者： Ranjitha Prasad Vincent Y. F. Tan TCS Innovation Labs Delhi India National University of Singapore Singapore

ISBN: (纸本)9781538638224

A popular approach to obtain a consensus ranking from ranking data is based on the probabilistic, distance-based Mallows model comprising of a modal permutation and dispersion parameters. Often, the population consists of several subpopulations. As a result, finite mixture models are used to distinguish latent sub-groups of individuals in a heterogeneous population. Given a finite number of subpopulations each based on the Mallows model, a popular inference approach is the computationally intensive expectation maximization algorithm for additive models. We address the drawbacks of this model using a novel multiplicative mixture Mallows model (M4). Given complete ranking observations from a heterogeneous population, we derive inference algorithms for the joint estimation of the parameters and the consensus rankings of the component distributions. We numerically validate the permutation estimation performance of the proposed algorithms on synthetic datasets. We also demonstrate the goodness-of-fit using the Bayesian information criterion and the integrated complete likelihood on the real-world APA and Sushi datasets.

关键词： Computational modeling inference algorithms Sociology Statistics Additives Data models Dispersion

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inference algorithms for generative score-spaces

Inference algorithms for generative score-spaces

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： A. Ragni M. J. F. Gales Engineering Department Cambridge University Cambridge UK

Using generative models, for example hidden Markov models (HMM), to derive features for a discriminative classifier has a number of advantages including the ability to make the features robust to speaker and noise changes. An interesting attribute of the derived features is that they may not have the same conditional independence assumptions as the underlying generative models, which are typically first-order Markovian. For efficiency these features are derived given a particular segmentation. This paper describes a general algorithm for obtaining the optimal segmentation with combined generative and discriminative models. Previous results, where the features were constrained to have first-order Markovian dependencies, are extended to allow derivative features to be used which are non-Markovian in nature. As an example, inference with zero and first-order HMM score-spaces is considered. Experimental results are presented on a noise-corrupted continuous digit string recognition task: AURORA 2.

关键词： Hidden Markov models Mathematical model Equations inference algorithms Feature extraction Adaptation models Training

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Review of Trends in Topic Modeling Techniques, Tools, inference algorithms and Applications

Review of Trends in Topic Modeling Techniques, Tools, Infere...

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International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Christine K. Mulunda Peter W. Wagacha Lawrence Muchemi School of Computing and Informatics University of Nairobi Nairobi Kenya

ISBN: (纸本)9781728113029;9781728113012

Topic modeling algorithms discover latent abstract topics in large volumes of data. The choice of inference algorithm determines the kind of solution the topic models will provide. inference algorithms aim at fast convergence of topics and stability of solutions with respect to initial parameters. The problem area identified for a topic model forms the basis of selecting the inference algorithm. This study reviewed 85 articles from various journals, classified them as reviews, techniques, tools, inference algorithms for topic models and application areas, among others. Scalability, computation speed and dynamism of topic models are necessary for robust solutions in analysis of datasets that are voluminous, varied and that scales up very fast with time.

关键词： Computational modeling inference algorithms Data models Tools Analytical models Real-time systems Approximation algorithms

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Unifying FSM-inference algorithms through declarative specification 13

Unifying FSM-inference algorithms through declarative specif...

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International Conference on Software Engineering (ICSE)

作者： Ivan Beschastnikh Yuriy Brun Jenny Abrahamson Michael D. Ernst Arvind Krishnamurthy Computer Science and Engineering University of Washington Seattle WA USA School of Computer Science University of Massachusetts Amherst MA USA

ISBN: (纸本)9781467330763

Logging system behavior is a staple development practice. Numerous powerful model inference algorithms have been proposed to aid developers in log analysis and system understanding. Unfortunately, existing algorithms are difficult to understand, extend, and compare. This paper presents InvariMint, an approach to specify model inference algorithms declaratively. We applied InvariMint to two model inference algorithms and present evaluation results to illustrate that InvariMint (1) leads to new fundamental insights and better understanding of existing algorithms, (2) simplifies creation of new algorithms, including hybrids that extend existing algorithms, and (3) makes it easy to compare and contrast previously published algorithms. Finally, algorithms specified with InvariMint can outperform their procedural versions.

关键词： inference algorithms Postal services Approximation algorithms Algorithm design and analysis Doped fiber amplifiers Educational institutions Electronic mail

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Assessing the usefulness of type inference algorithms in representing Java control flow to support software maintenance tasks

Assessing the usefulness of type inference algorithms in rep...

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International Conference on Software Maintenance (ICSM)

作者： Alex Kinneer Gregg Rothermel NVIDIA Corporation Austin TX USA Department of Computer Science and Engineering University of Nebraska Lincolnshire USA

A wide range of techniques for supporting software maintenance tasks rely on representations of program control flow. The accuracy of these representations can be important to the effectiveness and efficiency of these techniques. The Java programming language has introduced structured exception handling features that complicate the task of representing control flow. Previous work has attempted to address these complications by using type inference algorithms to analyze the control flow effects of exceptions, but to date, there has been no study of whether the use of these algorithms is justified. In this paper we report results of an empirical study addressing this issue. We find that type inference algorithms can lead to more accurate representations of control flow, but this improvement does not necessarily translate into benefits for maintenance techniques that use them. It follows that type inference algorithms should not just automatically be applied; rather, the tradeoffs of applying them must first be assessed with respect to particular maintenance techniques and workloads.

关键词： inference algorithms Algorithm design and analysis Flow graphs Java Time measurement Fluid flow measurement Awards activities

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Variational Learning and inference algorithms for Extended Gaussian Mixture Model

Variational Learning and Inference Algorithms for Extended G...

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IEEE/CIC International Conference on Communications in China

作者： Xin Wei Jianxin Chen Lei Wang Jingwu Cui Baoyu Zheng College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781479941452

In this paper, in order to properly evaluate the relative importance of priors and observed data in the Bayesian framework, we propose an extended Gaussian mixture model (EGMM) and design the corresponding learning inference algorithms. First, we define the likelihood function of the EGMM and then propose the variational learning algorithm for this EGMM. Moreover, the proposed model and approach are applied to speaker recognition. Experimental results demonstrate that this new approach generalizes the traditional GMM, offering a more powerful performance.

关键词： Speech Bayes methods Data models inference algorithms Speaker recognition Algorithm design and analysis Accuracy

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Generalized Innovation and inference algorithms for Hidden Mode Switched Linear Stochastic Systems with Unknown Inputs

Generalized Innovation and Inference Algorithms for Hidden M...

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IEEE Annual Conference on Decision and Control

作者： Sze Zheng Yong Minghui Zhu Emilio Frazzoli Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering Pennsylvania State University 201 Old Main University Park PA 16802 USA

ISBN: (纸本)9781467360890

In this paper, we propose inference algorithms for simultaneously estimating the mode, input and state of hidden mode switched linear stochastic systems with unknown inputs. First, we define the generalized innovation for the recently proposed optimal filter for simultaneous input and state estimation [1] and show that the sequence is a Gaussian white noise. Then, we utilize this whiteness property of the generalized innovation, which reflects the estimation quality to form the likelihood function of the system model. Consequently, we employ the multiple model (MM) approach based on the likelihood function for inferring the hidden mode of switched linear stochastic systems. algorithms for both static and dynamic MM estimation are presented and compared using a simulation example of vehicles at an intersection with switching driver intentions.

关键词： line stochastic system Likelihood Functions inference algorithms Technological Innovations Gaussian white noise optimal filters State estimation

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A Model of Multi-Cascade Fuzzy Logic Controller Implemented Using Different Variations of inference algorithms

A Model of Multi-Cascade Fuzzy Logic Controller Implemented ...

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International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)

作者： S.P. Cherniy A.V. Buzikayeva A.S. Gudim Federal State-financed Educational Institution of Higher Professional Learning “Komsomolsk-on-Amur State Technical University” Komsomolsk-on-Amur Russia

In the present work, the directions for expanding the intellectual capabilities of fuzzy control systems of complex technological objects are shown. Often, the operation of the control object is carried out either in the conditions of incomplete information, or significant deficiencies in the mathematical description, both in terms of the system of differential equations and in the restrictions imposed on the system as a whole. Overcoming these shortcomings with the use of typical fuzzy approaches is fraught with difficulties associated both with the algorithmic complexity of the knowledge base, the information redundancy of the linguistic variables used, and a significant number of fuzzy terms in a given control range. In the implementation of generally accepted control procedures based on soft calculations, difficulties arise with the implementation of the adaptive properties of such systems. The combination of various algorithms of fuzzy inference allows solving intellectual control problems for multicriteria and multifactor problems. At the same time, a different combination of such fuzzy conclusions enhances the various properties of such systems, for example, robustness, multitasking, aggregate control in the areas of large and small signals, etc. The paper presents models of two systems with a combination of algorithms for fuzzy logic inference Sugeno-Sugeno and Sugeno-Mamdani, analyzes the main characteristics of control systems using such multi-stage systems, identifies advantages and disadvantages for various combinations of algorithms, presents simulation results. In addition, some difficulties and features of setting up an external cascade, based on Sugeno's fuzzy logic inference algorithm, were identified, which can later be eliminated through the use of alternative output algorithms, for example, Mamdani. Features of the application of an algorithm in the internal cascade are most often dictated by the specifics of the operation of the control object,

关键词： inference algorithms Fuzzy logic Process control Switches Industrial engineering Knowledge based systems

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FlashSchema: Achieving High Quality XML Schemas with Powerful inference algorithms and Large-scale Schema Data

FlashSchema: Achieving High Quality XML Schemas with Powerfu...

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International Conference on Data Engineering

作者： Yeting Li Jialun Cao Haiming Chen Tingjian Ge Zhiwu Xu Qiancheng Peng State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China The Hong Kong University of Science and Technology Hong Kong China University of Massachusetts Lowell United States College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Getting high quality XML schemas to avoid or reduce application risks is an important problem in practice, for which some important aspects have yet to be addressed satisfactorily in existing work. In this paper, we propose a tool FlashSchema for high quality XML schema design, which supports both one-pass and interactive schema design and schema recommendation. To the best of our knowledge, no other existing tools support interactive schema design and schema recommendation. One salient feature of our work is the design of algorithms to infer k-occurrence interleaving regular expressions, which are not only more powerful in model capacity, but also more efficient. Additionally, such algorithms form the basis of our interactive schema design. The other feature is that, starting from large-scale schema data that we have harvested from the Web, we devise a new solution for type inference, as well as propose schema recommendation for schema design. Finally, we conduct a series of experiments on two XML datasets, comparing with 9 state-of-the-art algorithms and open-source tools in terms of running time, preciseness, and conciseness. Experimental results show that our work achieves the highest level of preciseness and conciseness within only a few seconds. Experimental results and examples also demonstrate the effectiveness of our type inference and schema recommendation methods.

关键词： XML inference algorithms Tools Semantics Libraries Data models

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