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VAE-KRnet and its Applications to variational bayes

Communications in Computational Physics 2022年第4期31卷 1049-1082页

作者： Xiaoliang Wan Shuangqing Wei Department of Mathematics Center for Computation and TechnologyLouisiana State UniversityBaton Rouge 70803USA Division of Electrical&Computer Engineering Louisiana State UniversityBaton Rouge 70803USA

In this work,we have proposed a generative model,called VAE-KRnet,for density estimation or approximation,which combines the canonical variational autoencoder(VAE)with our recently developed flow-based generativemodel,called *** is used as a dimension reduction technique to capture the latent space,and KRnet is used to model the distribution of the latent *** a linear model between the data and the latent variable,we show that VAE-KRnet can be more effective and robust than the canonical ***-KRnet can be used as a density model to approximate either data distribution or an arbitrary probability density function(PDF)known up to a ***-KRnet is flexible in terms of *** the number of dimensions is relatively small,KRnet can effectively approximate the distribution in terms of the original random *** high-dimensional cases,we may use VAE-KRnet to incorporate dimension *** important application of VAE-KRnet is the variational bayes for the approximation of the posterior *** variational bayes approaches are usually based on the minimization of the Kullback-Leibler(KL)divergence between the model and the *** highdimensional distributions,it is very challenging to construct an accurate densitymodel due to the curse of dimensionality,where extra assumptions are often introduced for *** instance,the classical mean-field approach assumes mutual independence between dimensions,which often yields an underestimated variance due to *** alleviate this issue,we include into the loss the maximization of the mutual information between the latent random variable and the original random variable,which helps keep more information from the region of low density such that the estimation of variance is *** experiments have been presented to demonstrate the effectiveness of our model.

关键词： Deep learning variational bayes uncertainty quantification bayesian inverse problems generative modeling

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Distributed variational bayes Based on Consensus of Probability Densities

Distributed Variational Bayes Based on Consensus of Probabil...

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第三十九届中国控制会议

作者： Peng Lin Chen Hu Yu Lou China United Network Communications Group Company Limited Beijing University of Posts and Telecommunications Rocket Force University of Engineering

This paper discusses a distributed design for the variational bayes problem in a time-varying multi-agent *** this circumstance, all the data are distributed stored and unavailable to all agents and each agent has to update based on its own computation and communication with neighbors. By virtue of distributed subgradient methods and consensus of probability densities idea, a distributed framework for variational bayesian over exponential family is introduced, which can be implemented only based on local datasets. Simulations on synthetic datasets are presented to illustrate the effectiveness of the algorithm empirically, even though the communication network topology is time-varying.

关键词： variational bayes exponential family K-L divergence distributed learning multi-agent

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Effective Frameworks Based on Infinite Mixture Model for Real-World Applications

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Computers, Materials & Continua 2022年第7期72卷 1139-1156页

作者： Norah Saleh Alghamdi Sami Bourouis Nizar Bouguila Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia College of Computers and Information Technology Taif UniversityTaif21944Saudi Arabia The Concordia Institute for Information Systems Engineering(CIISE) Concordia UniversityMontrealQC H3G 1T7Canada

Interest in automated data classification and identification systems has increased over the past years in conjunction with the high demand for artificial intelligence and security *** particular,recognizing human activities with accurate results have become a topic of high *** the current tools have reached remarkable successes,it is still a challenging problem due to various uncontrolled environments and *** this paper two statistical frameworks based on nonparametric hierarchical bayesian models and Gamma distribution are proposed to solve some realworld *** particular,two nonparametric hierarchical bayesian models based on Dirichlet process and Pitman-Yor process are *** models are then applied to address the problem of modelling grouped data where observations are organized into groups and these groups are statistically linked by sharing mixture *** choice of the Gamma mixtures is motivated by its flexibility for modelling heavy-tailed *** addition,deploying the Dirichlet process prior is justified by its advantage of automatically finding the right number of components and providing nice ***,a learning step via variational bayesian setting is presented in a flexible *** priors over the parameters are selected appropriately and the posteriors are approximated effectively in a closed *** results based on a real-life applications that concerns texture classification and human actions recognition show the capabilities and effectiveness of the proposed framework.

关键词： Infinite Gamma mixture model variational bayes hierarchical Dirichlet process Pitman-Yor process texture classification human action recognition

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An EKF Algorithm Based on a Skew-t Measurement Noise Model

An EKF Algorithm Based on a Skew-t Measurement Noise Model

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第36届中国控制会议

作者： Zongyuan Wang Weidong Zhou Ling Xue Huanzhang Ling Tao Chen College of Science Harbin Engineering University College of Automation Harbin Engineering University

In many practical problems,measurement noise data are often skewed,heterogeneous,or containing *** Gaussian or even Student-t measurement noise settings are often not fit to the request of system.A novel EKF filtering algorithm for nonlinear discrete state-space models with skewed Student-t measurement noise is *** algorithm makes use of the method of variational bayes to make approximation of the posterior of system state based on the known measurement noise *** prediction uses the standard step of *** on the model of Signal tracking with skew-t measurement noise indicates the proposed algorithm outperforms the standard EKF for the set noise conditions.

关键词： skew t-distribution Extended Kalman Filter variational bayes robust filtering

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Superposition Estimation of Harmonic Current of Charging Station Based on Variable bayesian

Superposition Estimation of Harmonic Current of Charging Sta...

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第37届中国控制会议

作者： Huang Yonghong Zhou Jie Xu Zhen School of Electrical and Information Engineering Jiangsu University State Grid Yancheng Power Supply Company

With the gradual expansion of the scale of the charging station, the influence of the harmonic current on the distribution network becomes particularly serious when it is put into use. In the practical application, the traditional national standard harmonic superposition algorithm has a deviation from the actual harmonics because of the influence of various factors in the charging station. To solve this problem, the variational bayesian posterior parameter estimation method is applied to harmonic current superposition algorithm, this method by measuring lower bound space continuously maximum marginal likelihood function in the state space and the amount of harmonic phase, iterative updates of variational phase parameters, until the approximate distribution of the true posterior approximation parameter distribution, and then realize the accurate detection of the superposition of harmonic phase. The experimental simulation shows that the variational bayesian estimation method is more effective and accurate for the superposition of the phase parameters which obey Gauss distribution than the traditional harmonic superposition algorithm, and provides an effective basis for the precise harmonic suppression.

关键词： Charging Station Harmonic Superposition variational bayes Gaussian Distribution

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