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Splitting models for multivariate count data

JOURNAL OF MULTIVARIATE ANALYSIS 2021年 181卷 104677-104677页

作者： Peyhardi, Jean Fernique, Pierre Durand, Jean-Baptiste Univ Montpellier CNRS IMAG F-34090 Montpellier France Chappes Res Ctr Limagrain Field Seeds Res Biostat Dept Chappes France Univ Grenoble Alpes CNRS Grenoble INP INRIAInst EngnLJK F-38000 Grenoble France

We investigate the class of splitting distributions as the composition of a singular multivariate distribution and a univariate distribution. It will be shown that most common parametric count distributions (multinomial, negative multinomial, multivariate hypergeometric, multivariate negative hypergeometric, ...) can be written as splitting distributions with separate parameters for both components, thus facilitating their interpretation, inference, the study of their probabilistic characteristics and their extensions to regression models. We highlight many probabilistic properties deriving from the compound aspect of splitting distributions and their underlying algebraic properties. Parameter inference and model selection are thus reduced to two separate problems, preserving time and space complexity of the base models. Based on this principle, we introduce several new distributions. In the case of multinomial splitting distributions, conditional independence and asymptotic normality properties for estimators are obtained. Mixtures of splitting regression models are used on a mango tree dataset in order to analyze the patchiness. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Compound distribution Multivariate extension probabilistic graphical model Singular multivariate distribution

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Exploiting noise as a resource for computation and learning in spiking neural networks

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PATTERNS 2023年第10期4卷 100831页

作者： Ma, Gehua Yan, Rui Tang, Huajin Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Peoples R China Zhejiang Univ State Key Lab Brain Machine Intelligence Hangzhou Peoples R China

Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have become pillar models in neuromorphic artificial intelligence. Despite extensive research on spiking neural networks (SNNs), most studies are established on deterministic models, overlooking the inherent nondeterministic, noisy nature of neural computations. This study introduces the noisy SNN (NSNN) and the noise-driven learning (NDL) rule by incorporating noisy neuronal dynamics to exploit the computational advantages of noisy neural processing. The NSNN provides a theoretical framework that yields scalable, flexible, and reliable computation and learning. We demonstrate that this framework leads to spiking neural models with competitive performance, improved robustness against challenging perturbations compared with deterministic SNNs, and better reproducing probabilistic computation in neural coding. Generally, this study offers a powerful and easy-to-use tool for machine learning, neuromorphic intelligence practitioners, and computational neuroscience researchers.

关键词： spiking neural network noisy spiking neural network surrogate gradient noise-driven learning neuromorphic intelligence neural coding probabilistic graphical model dynamic system DSML 2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem

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A computational framework for boosting confidence in high-throughput protein-protein interaction datasets

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GENOME BIOLOGY 2012年第8期13卷 2999-2999页

作者： Hosur, Raghavendra Peng, Jian Vinayagam, Arunachalam Stelzl, Ulrich Xu, Jinbo Perrimon, Norbert Bienkowska, Jadwiga Berger, Bonnie MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA Toyota Technol Inst Chicago IL 60637 USA Harvard Univ Sch Med Dept Genet Boston MA 02115 USA Max Planck Inst Mol Genet Otto Warburg Lab D-14195 Berlin Germany Howard Hughes Med Inst Boston MA 02115 USA Biogen Idec Inc Computat Biol Grp Cambridge MA 02142 USA MIT Dept Math Cambridge MA 02139 USA

Improving the quality and coverage of the protein interactome is of tantamount importance for biomedical research, particularly given the various sources of uncertainty in high-throughput techniques. We introduce a structure-based framework, Coev2Net, for computing a single confidence score that addresses both false-positive and false-negative rates. Coev2Net is easily applied to thousands of binary protein interactions and has superior predictive performance over existing methods. We experimentally validate selected high-confidence predictions in the human MAPK network and show that predicted interfaces are enriched for cancer -related or damaging SNPs. Coev2Net can be downloaded at http:***.

关键词： Markov Chain Monte Carlo Protein Data Bank Confidence Score Interface Residue probabilistic graphical model

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Reconstructing Transcriptional Regulatory Networks Using Three-Way Mutual Information and Bayesian Networks

Reconstructing Transcriptional Regulatory Networks Using Thr...

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作者： Weijun Luo Peter J. Woolf

probabilistic methods such as mutual information and Bayesian networks have become a major category of tools for the reconstruction of regulatory relationships from quantitative biological data. In this chapter, we describe the theoretic framework and the implementation for learning gene regulatory networks using high-order mutual information via the MI3 method (Luo et al. (2008) BMC Bioinformatics 9, 467; Luo (2008) Gene regulatory network reconstruction and pathway inference from high throughput gene expression data. PhD thesis). We also cover the closely related Bayesian network method in detail. less

关键词： Microarray Gene Expression Analysis Genome Annotation systems biology Bayesian network gene expression data transcriptional regulation Three-way mutual information high-order mutual information information theory probabilistic graphical model gene regulatory network (GRN)

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Combination of graphics, uncertainty, and semantics: A survey

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第7期34卷

作者： Gao, Yuan Rafi, Muhammad Adnan Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Beijing Peoples R China Aston Univ Dept Comp Sci Birmingham W Midlands England

Graphics, uncertainty, and semantics are three approaches to building models. The combination of the three approaches is a way to develop a stronger modeling method. This article surveys the research efforts toward combining these aspects, which can be divided into two routes: One is to combine graphics and uncertainty as probabilistic graphical models and then incorporate semantics, and the other is to combine graphics and semantics and then incorporate probability to handle uncertainty. The models and methods involved in these efforts are introduced and their expressiveness, pros, and cons are discussed.

关键词： computer science graph-based knowledge representation knowledge representation probabilistic graphical model probability

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Research on the Challenges of Machine Learning

Research on the Challenges of Machine Learning

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作者： Xiaohan SUN Kai WANG WeiFang NO.1 Middle School Baidu

This study starts from the perspective of the challenges of machine learning, mainly explores the high-dimensional spatial structure and the overall data, and analyzes with the number of calculated data to find the optimal solution. And then, this paper explores combined with the current hot topic that people concerned about to make people put more attention on the machine learning.

关键词： Machine learning Deep learning probabilistic graphical model Big data Optimization problem

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Role-aware Conformity Influence Analysis in Recommender Systems 17

Role-aware Conformity Influence Analysis in Recommender Syst...

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Proceedings of the 20th International Workshop on the Web and Databases

作者： Mengzi Tang Li Li Southwest University Chongqing China

ISBN: (纸本)9781450349833

Recommender systems play an important role in providing personalized information to users and helping address the information overload problem. Recent research has considered social theories and studied the importance of social influence in social recommendation systems. However, many publications ignored the users' roles information or just considered some single roles. In fact, users often have many different roles. Besides, different types of users (users with different roles) might have different conformity tendency. Thus, this inspires us to study how conformity tendency changes with users' roles in recommender systems. We firstly formalize conformity influence by defining a utility function and then propose a probabilistic graphical model integrating both users' roles and conformity tendency, named as Role Conformity Recommender Systems (RCRS). We evaluate the proposed model on several real-world datasets. The experimental results show that our model significantly outperforms state-of-the-art approaches.

关键词： Users roles Social conformity Recommender systems probabilistic graphical model

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A CONCEPT FOR BUILDING RECONSTRUCTION FROM AIRBORNE MULTI-ASPECT SAR DATA

A CONCEPT FOR BUILDING RECONSTRUCTION FROM AIRBORNE MULTI-AS...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Oliver Maksymiuk Uwe Stilla Photogrammetry & Remote Sensing Technische Universitaet Muenchen (TUM)

ISBN: (纸本)9781467311601

In this work we present a new concept towards building reconstruction based on multi-aspect SAR (MASAR) data where we make use of the inherent redundancy and contradiction of MASAR data. Instead of treating every aspect seperately we perform a joint optimization on all available data. For this purpose we use probabilistic graphical model (PGM) as a declarative representation to encode our knowledge and to reconstruct the most likely objects in the scene.

关键词： Building Reconstruction Multi-Aspect SAR Interferometry Radargrammetry probabilistic graphical model Conditional Random Field BUILDING RENOVATION Buildings Rescue Synthetic aperture radar Specific absorption rate SAFETY ANALYSIS REPORTS available data Reconstructions WWOX gene graphical models

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Analysis and Research models of the Estimation of Distribution Algorithms

Analysis and Research Models of the Estimation of Distributi...

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2011 International Conference on Computer Science and Network Technology(2011计算机科学与网络技术国际会议 ICCSNT 2011)

作者： Lihong Bi Shumin Zhang Shijiangzhuang Institute of Railway Technology Shijiazhuang Hebei China Shij iangzhuang Institute of Railway Technology Shijiazhuang Hebei China

ISBN: (纸本)9781457715860

The Estimation of Distribution Algorithms (EDAs) is a novel class of evolutionary algorithms which is motivated by the idea of building probabilistic graphical model of promising solutions to represent linkage information between variables in chromosome. Through learning of and sampling from probabilistic graphical model, new population is generated and optimization procedure is repeated until the stopping criteria are *** this paper, the mechanism of the Estimation of Distribution Algorithms is *** existing EDAs are surveyed and categorized according to the probabilistic model they used,then the strengths and weakness and the future perspective of EDAs are concluded.

关键词： Estimation of Distribution Algorithms Genetic Algorithms probabilistic graphical model

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Continuous visual speech recognition for audio speech enhancement

Continuous visual speech recognition for audio speech enhanc...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： E. Benhaim H. Sahbi G. Vittey LTCI Telecom ParisTech Paris France

ISBN: (纸本)9781467369985

We introduce in this paper a novel non-blind speech enhancement procedure based on visual speech recognition (VSR). The latter is based on a generative process that analyzes sequences of talking faces and classifies them into visual speech units known as visemes. We use an effective graphical model able to segment and label a given sequence of talking faces into a sequence of visemes. Our model captures unary potential as well as pairwise interaction;the former models visual appearance of speech units while the latter models their interactions using boundary and visual language model activations. Experiments conducted on a standard challenging dataset, show that when feeding the results of VSR to the speech enhancement procedure, it clearly outperforms baseline blind methods as well as related work.

关键词： Visual speech recognition belief propagation model-based speech enhancement probabilistic graphical model Speech enhancement Belief propagation Speech recognition Vibratory stress relieving probabilistic model Visualization Speaking Audio

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