检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Empirically determining the sample size for large-scale gene network inference algorithms

引用

IET SYSTEMS BIOLOGY 2012年第2期6卷 35-U28页

作者： Altay, G. Univ Cambridge CRUK Cambridge Res Inst Dept Oncol Cambridge CB2 0RE England Bahcesehir Univ Dept Elect Elect Engn TR-34353 Istanbul Turkey

The performance of genome-wide gene regulatory network inference algorithms depends on the sample size. It is generally considered that the larger the sample size, the better the gene network inference performance. Nevertheless, there is not adequate information on determining the sample size for optimal performance. In this study, the author systematically demonstrates the effect of sample size on information-theory-based gene network inference algorithms with an ensemble approach. The empirical results showed that the inference performances of the considered algorithms tend to converge after a particular sample size region. As a specific example, the sample size region around similar or equal to 64 is sufficient to obtain the most of the inference performance with respect to precision using the representative algorithm C3NET on the synthetic steady-state data sets of Escherichia coli and also time-series data set of a homo sapiens subnetworks. The author verified the convergence result on a large, real data set of E. coli as well. The results give evidence to biologists to better design experiments to infer gene networks. Further, the effect of cutoff on inference performances over various sample sizes is considered.

关键词： Sample Size Escherichia coli inference algorithms time series data Homo sapiens Gene Regulatory Networks

来源：评论

学校读者我要写书评

暂无评论

inference algorithms for the Multiplicative Mixture Mallows Model

Inference Algorithms for the Multiplicative Mixture Mallows ...

引用

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

来源：评论

学校读者我要写书评

暂无评论

inference algorithms for generative score-spaces

Inference algorithms for generative score-spaces

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Unifying FSM-inference algorithms through declarative specification 13

Unifying FSM-inference algorithms through declarative specif...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Variational Learning and inference algorithms for Extended Gaussian Mixture Model

Variational Learning and Inference Algorithms for Extended G...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

Generalized Innovation and Inference Algorithms for Hidden M...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：