Relevant bibliographies by topics / Gaussian process mixture model

Academic literature on the topic 'Gaussian process mixture model'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Gaussian process mixture model.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Gaussian process mixture model"

1

Tayal, Aditya, Pascal Poupart, and Yuying Li. "Hierarchical Double Dirichlet Process Mixture of Gaussian Processes." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 1126–33. http://dx.doi.org/10.1609/aaai.v26i1.8309.

Full text
Abstract:
We consider an infinite mixture model of Gaussian processes that share mixture components between non-local clusters in data. Meeds and Osindero (2006) use a single Dirichlet process prior to specify a mixture of Gaussian processes using an infinite number of experts. In this paper, we extend this approach to allow for experts to be shared non-locally across the input domain. This is accomplished with a hierarchical double Dirichlet process prior, which builds upon a standard hierarchical Dirichlet process by incorporating local parameters that are unique to each cluster while sharing mixture components between them. We evaluate the model on simulated and real data, showing that sharing Gaussian process components non-locally can yield effective and useful models for richly clustered non-stationary, non-linear data.
APA, Harvard, Vancouver, ISO, and other styles
2

Savitsky, Terrance, and Marina Vannucci. "Spiked Dirichlet Process Priors for Gaussian Process Models." Journal of Probability and Statistics 2010 (2010): 1–14. http://dx.doi.org/10.1155/2010/201489.

Full text
Abstract:
We expand a framework for Bayesian variable selection for Gaussian process (GP) models by employing spiked Dirichlet process (DP) prior constructions over set partitions containing covariates. Our approach results in a nonparametric treatment of the distribution of the covariance parameters of the GP covariance matrix that in turn induces a clustering of the covariates. We evaluate two prior constructions: the first one employs a mixture of a point-mass and a continuous distribution as the centering distribution for the DP prior, therefore, clustering all covariates. The second one employs a mixture of a spike and a DP prior with a continuous distribution as the centering distribution, which induces clustering of the selected covariates only. DP models borrow information across covariates through model-based clustering. Our simulation results, in particular, show a reduction in posterior sampling variability and, in turn, enhanced prediction performances. In our model formulations, we accomplish posterior inference by employing novel combinations and extensions of existing algorithms for inference with DP prior models and compare performances under the two prior constructions.
APA, Harvard, Vancouver, ISO, and other styles
3

Ay, Fahrettin, Gökhan İnce, Mustafa E. Kamaşak, and K. Yavuz Ekşi. "Classification of pulsars with Dirichlet process Gaussian mixture model." Monthly Notices of the Royal Astronomical Society 493, no. 1 (January 17, 2020): 713–22. http://dx.doi.org/10.1093/mnras/staa154.

Full text
Abstract:
ABSTRACT Young isolated neutron stars (INSs) most commonly manifest themselves as rotationally powered pulsars that involve conventional radio pulsars as well as gamma-ray pulsars and rotating radio transients. Some other young INS families manifest themselves as anomalous X-ray pulsars and soft gamma-ray repeaters that are commonly accepted as magnetars, i.e. magnetically powered neutron stars with decaying super-strong fields. Yet some other young INSs are identified as central compact objects and X-ray dim isolated neutron stars that are cooling objects powered by their thermal energy. Older pulsars, as a result of a previous long episode of accretion from a companion, manifest themselves as millisecond pulsars and more commonly appear in binary systems. We use Dirichlet process Gaussian mixture model (DPGMM), an unsupervised machine learning algorithm, for analysing the distribution of these pulsar families in the parameter space of period and period derivative. We compare the average values of the characteristic age, magnetic dipole field strength, surface temperature, and transverse velocity of all discovered clusters. We verify that DPGMM is robust and provide hints for inferring relations between different classes of pulsars. We discuss the implications of our findings for the magnetothermal spin evolution models and fallback discs.
APA, Harvard, Vancouver, ISO, and other styles
4

Yu, Jie, and S. Joe Qin. "Multiway Gaussian Mixture Model Based Multiphase Batch Process Monitoring." Industrial & Engineering Chemistry Research 48, no. 18 (September 16, 2009): 8585–94. http://dx.doi.org/10.1021/ie900479g.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Chen, Shutian, Qingchao Jiang, and Xuefeng Yan. "Multimodal process monitoring based on transition-constrained Gaussian mixture model." Chinese Journal of Chemical Engineering 28, no. 12 (December 2020): 3070–78. http://dx.doi.org/10.1016/j.cjche.2020.08.021.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

ZHANG, FENG, and ZHUJUN WENG. "MIXTURE PRINCIPAL COMPONENT ANALYSIS MODEL FOR MULTIVARIATE PROCESSES MONITORING." Journal of Advanced Manufacturing Systems 04, no. 02 (December 2005): 151–66. http://dx.doi.org/10.1142/s0219686705000631.

Full text
Abstract:
A mixture probabilistic principal component analysis model is proposed as a process monitoring tool in this paper. High-dimensional measurement data could be aggregated into some clusters based on the mixture distribution model, where the number of these clusters are automatically determined from the maximum likelihood estimation procedures. It was illustrated that the mixture PCA models conform to the multivariate data well in the experiments involving Gaussian mixtures. The multivariate statistical process monitoring mechanism is then developed first with the learning of a finite mixture model with variant principal component within each cluster, followed by the construction of the statistical process confidence intervals for the identified regions or nodes from T2 charts. For the abnormal input measurement, they would fall out of the acceptance region set by the confidence control limits.
APA, Harvard, Vancouver, ISO, and other styles
7

Guo, Wei, Tianhong Pan, Zhengming Li, and Shan Chen. "Batch process modeling by using temporal feature and Gaussian mixture model." Transactions of the Institute of Measurement and Control 42, no. 6 (December 1, 2019): 1204–14. http://dx.doi.org/10.1177/0142331219887827.

Full text
Abstract:
Multi-model/multi-phase modeling algorithm has been widely used to monitor the product quality in complicated batch processes. Most multi-model/ multi-phase modeling methods hinge on the structure of a linearly separable space or a combination of different sub-spaces. However, it is impossible to accurately separate the overlapping region samples into different operating sub-spaces using unsupervised learning techniques. A Gaussian mixture model (GMM) using temporal features is proposed in the work. First, the number of sub-model is estimated by using the maximum interval process trend analysis algorithm. Then, the GMM parameters constrained with the temporal value are identified by using the expectation maximization (EM) algorithm, which minimizes confusion in overlapping regions of different Gaussian processes. A numerical example and a penicillin fermentation process demonstrate the effectiveness of the proposed algorithm.
APA, Harvard, Vancouver, ISO, and other styles
8

Zheng, Junhua, Qiaojun Wen, and Zhihuan Song. "Recursive Gaussian Mixture Models for Adaptive Process Monitoring." Industrial & Engineering Chemistry Research 58, no. 16 (April 2019): 6551–61. http://dx.doi.org/10.1021/acs.iecr.8b06101.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Yuhan, Zhao. "Gaussian process mixture model for prediction based on maximum posterior distribution." Journal of Physics: Conference Series 2014, no. 1 (September 1, 2021): 012007. http://dx.doi.org/10.1088/1742-6596/2014/1/012007.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Li, Ling-Ling, Jin Sun, Ching-Hsin Wang, Ya-Tong Zhou, and Kuo-Ping Lin. "Enhanced Gaussian process mixture model for short-term electric load forecasting." Information Sciences 477 (March 2019): 386–98. http://dx.doi.org/10.1016/j.ins.2018.10.063.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Gaussian process mixture model"

1

Zhang, Lin. "Semiparametric Bayesian Kernel Survival Model for Highly Correlated High-Dimensional Data." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/95040.

Full text
Abstract:
We are living in an era in which many mysteries related to science, technologies and design can be answered by "learning" the huge amount of data accumulated over the past few decades. In the processes of those endeavors, highly-correlated high-dimensional data are frequently observed in many areas including predicting shelf life, controlling manufacturing processes, and identifying important pathways related with diseases. We define a "set" as a group of highly-correlated high-dimensional (HCHD) variables that possess a certain practical meaning or control a certain process, and define an "element" as one of the HCHD variables within a certain set. Such an elements-within-a-set structure is very complicated because: (i) the dimensions of elements in different sets can vary dramatically, ranging from two to hundreds or even thousands; (ii) the true relationships, include element-wise associations, set-wise interactions, and element-set interactions, are unknown; (iii) and the sample size (n) is usually much smaller than the dimension of the elements (p). The goal of this dissertation is to provide a systematic way to identify both the set effects and the element effects associated with survival outcomes from heterogeneous populations using Bayesian survival kernel models. By connecting kernel machines with semiparametric Bayesian hierarchical models, the proposed unified model frameworks can identify significant elements as well as sets regardless of mis-specifications of distributions or kernels. The proposed methods can potentially be applied to a vast range of fields to solve real-world problems.
PHD
APA, Harvard, Vancouver, ISO, and other styles
2

Xu, Li. "Statistical Methods for Variability Management in High-Performance Computing." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104184.

Full text
Abstract:
High-performance computing (HPC) variability management is an important topic in computer science. Research topics include experimental designs for efficient data collection, surrogate models for predicting the performance variability, and system configuration optimization. Due to the complex architecture of HPC systems, a comprehensive study of HPC variability needs large-scale datasets, and experimental design techniques are useful for improved data collection. Surrogate models are essential to understand the variability as a function of system parameters, which can be obtained by mathematical and statistical models. After predicting the variability, optimization tools are needed for future system designs. This dissertation focuses on HPC input/output (I/O) variability through three main chapters. After the general introduction in Chapter 1, Chapter 2 focuses on the prediction models for the scalar description of I/O variability. A comprehensive comparison study is conducted, and major surrogate models for computer experiments are investigated. In addition, a tool is developed for system configuration optimization based on the chosen surrogate model. Chapter 3 conducts a detailed study for the multimodal phenomena in I/O throughput distribution and proposes an uncertainty estimation method for the optimal number of runs for future experiments. Mixture models are used to identify the number of modes for throughput distributions at different configurations. This chapter also addresses the uncertainty in parameter estimation and derives a formula for sample size calculation. The developed method is then applied to HPC variability data. Chapter 4 focuses on the prediction of functional outcomes with both qualitative and quantitative factors. Instead of a scalar description of I/O variability, the distribution of I/O throughput provides a comprehensive description of I/O variability. We develop a modified Gaussian process for functional prediction and apply the developed method to the large-scale HPC I/O variability data. Chapter 5 contains some general conclusions and areas for future work.
Doctor of Philosophy
This dissertation focuses on three projects that are all related to statistical methods in performance variability management in high-performance computing (HPC). HPC systems are computer systems that create high performance by aggregating a large number of computing units. The performance of HPC is measured by the throughput of a benchmark called the IOZone Filesystem Benchmark. The performance variability is the variation among throughputs when the system configuration is fixed. Variability management involves studying the relationship between performance variability and the system configuration. In Chapter 2, we use several existing prediction models to predict the standard deviation of throughputs given different system configurations and compare the accuracy of predictions. We also conduct HPC system optimization using the chosen prediction model as the objective function. In Chapter 3, we use the mixture model to determine the number of modes in the distribution of throughput under different system configurations. In addition, we develop a model to determine the number of additional runs for future benchmark experiments. In Chapter 4, we develop a statistical model that can predict the throughout distributions given the system configurations. We also compare the prediction of summary statistics of the throughput distributions with existing prediction models.
APA, Harvard, Vancouver, ISO, and other styles
3

Erich, Roger Alan. "Regression Modeling of Time to Event Data Using the Ornstein-Uhlenbeck Process." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1342796812.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Bjarnason, Brynjar Smári. "Clustering metagenome contigs using coverage with CONCOCT." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208944.

Full text
Abstract:
Metagenomics allows studying genetic potentials of microorganisms without prior cultivation. Since metagenome assembly results in fragmented genomes, a key challenge is to cluster the genome fragments (contigs) into more or less complete genomes. The goal of this project was to investigate how well CONCOCT bins assembled contigs into taxonomically relevant clusters using the abundance profiles of the contigs over multiple samples. This was done by studying the effects of different parameter settings for CONCOCT on the clustering results when clustering metagenome contigs from in silico model communities generated by mixing data from isolate genomes. These parameters control how the model that CONCOCT trains is tuned and then how the model fits contigs to their cluster. Each parameter was tested in isolation while others were kept at their default values. For each of the data set used, the number of clusters was kept constant at the known number of species and strains in their respective data set. The resulting configuration was to use a tied covariance model, using principal components explaining 90% of the variance, and filtering out contigs shorter than 3000 bp. It also suggested that all available samples should be used for the abundance profiles. Using these parameters for CONCOCT, it was executed to have it estimate the number of clusters automatically. This gave poor results which lead to the conclusion that the process for selecting the number of clusters that was implemented in CONCOCT, “Bayesian Information Criterion”, was not good enough. That led to the testing of another similar mathematical model, “Dirichlet Process Gaussian Mixture Model”, that uses a different algorithm to estimate number of clusters. This new model gave much better results and CONCOCT has adapted a similar model in later versions.
Metagenomik möjliggör analys av arvsmassor i mikrobiella floror utan att först behöva odla mikroorgansimerna. Metoden innebär att man läser korta DNA-snuttar som sedan pusslas ihop till längre genomfragment (kontiger). Genom att gruppera kontiger som härstammar från samma organism kan man sedan återskapa mer eller mindre fullständiga genom, men detta är en svår bioinformatisk utmaning. Målsättningen med det här projektet var att utvärdera precisionen med vilken mjukvaran CONCOCT, som vi nyligen utvecklat, grupperar kontiger som härstammar från samma organism baserat på information om kontigernas sekvenskomposition och abundansprofil över olika prover. Vi testade hur olika parametrar påverkade klustringen av kontiger i artificiella metagenomdataset av olika komplexitet som vi skapade in silico genom att blanda data från tidigare sekvenserade genom. Parametrarna som testades rörde indata såväl som den statistiska modell som CONCOCT använder för att utföra klustringen. Parametrarna varierades en i taget medan de andra parametrarna hölls konstanta. Antalet kluster hölls också konstant och motsvarade antalet olika organismer i flororna. Bäst resultat erhölls då vi använde en låst kovariansmodell och använde principalkomponenter som förklarade 90% av variansen, samt filtrerade bort kontiger som var kortare än 3000 baspar. Vi fick också bäst resultat då vi använde alla tillgängliga prover. Därefter använde vi dessa parameterinställningar och lät CONCOCT själv bestämma lämpligt antal kluster i dataseten med “Bayesian Information Criterion” - metoden som då var implementerad i CONCOCT. Detta gav otillfredsställande resultat med i regel för få och för stora kluster. Därför testade vi en alternativ metod, “Dirichlet Process Gaussian Mixture Model”, för att uppskatta antal kluster. Denna metod gav avsevärt bättre resultat och i senare versioner av CONCOCT har en liknande metod implementerats.
APA, Harvard, Vancouver, ISO, and other styles
5

Tang, Man. "Statistical methods for variant discovery and functional genomic analysis using next-generation sequencing data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/104039.

Full text
Abstract:
The development of high-throughput next-generation sequencing (NGS) techniques produces massive amount of data, allowing the identification of biomarkers in early disease diagnosis and driving the transformation of most disciplines in biology and medicine. A greater concentration is needed in developing novel, powerful, and efficient tools for NGS data analysis. This dissertation focuses on modeling ``omics'' data in various NGS applications with a primary goal of developing novel statistical methods to identify sequence variants, find transcription factor (TF) binding patterns, and decode the relationship between TF and gene expression levels. Accurate and reliable identification of sequence variants, including single nucleotide polymorphisms (SNPs) and insertion-deletion polymorphisms (INDELs), plays a fundamental role in NGS applications. Existing methods for calling these variants often make simplified assumption of positional independence and fail to leverage the dependence of genotypes at nearby loci induced by linkage disequilibrium. We propose vi-HMM, a hidden Markov model (HMM)-based method for calling SNPs and INDELs in mapped short read data. Simulation experiments show that, under various sequencing depths, vi-HMM outperforms existing methods in terms of sensitivity and F1 score. When applied to the human whole genome sequencing data, vi-HMM demonstrates higher accuracy in calling SNPs and INDELs. One important NGS application is chromatin immunoprecipitation followed by sequencing (ChIP-seq), which characterizes protein-DNA relations through genome-wide mapping of TF binding sites. Multiple TFs, binding to DNA sequences, often show complex binding patterns, which indicate how TFs with similar functionalities work together to regulate the expression of target genes. To help uncover the transcriptional regulation mechanism, we propose a novel nonparametric Bayesian method to detect the clustering pattern of multiple-TF bindings from ChIP-seq datasets. Simulation study demonstrates that our method performs best with regard to precision, recall, and F1 score, in comparison to traditional methods. We also apply the method on real data and observe several TF clusters that have been recognized previously in mouse embryonic stem cells. Recent advances in ChIP-seq and RNA sequencing (RNA-Seq) technologies provides more reliable and accurate characterization of TF binding sites and gene expression measurements, which serves as a basis to study the regulatory functions of TFs on gene expression. We propose a log Gaussian cox process with wavelet-based functional model to quantify the relationship between TF binding site locations and gene expression levels. Through the simulation study, we demonstrate that our method performs well, especially with large sample size and small variance. It also shows a remarkable ability to distinguish real local feature in the function estimates.
Doctor of Philosophy
The development of high-throughput next-generation sequencing (NGS) techniques produces massive amount of data and bring out innovations in biology and medicine. A greater concentration is needed in developing novel, powerful, and efficient tools for NGS data analysis. In this dissertation, we mainly focus on three problems closely related to NGS and its applications: (1) how to improve variant calling accuracy, (2) how to model transcription factor (TF) binding patterns, and (3) how to quantify of the contribution of TF binding on gene expression. We develop novel statistical methods to identify sequence variants, find TF binding patterns, and explore the relationship between TF binding and gene expressions. We expect our findings will be helpful in promoting a better understanding of disease causality and facilitating the design of personalized treatments.
APA, Harvard, Vancouver, ISO, and other styles
6

Fang, Zaili. "Some Advanced Model Selection Topics for Nonparametric/Semiparametric Models with High-Dimensional Data." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/40090.

Full text
Abstract:
Model and variable selection have attracted considerable attention in areas of application where datasets usually contain thousands of variables. Variable selection is a critical step to reduce the dimension of high dimensional data by eliminating irrelevant variables. The general objective of variable selection is not only to obtain a set of cost-effective predictors selected but also to improve prediction and prediction variance. We have made several contributions to this issue through a range of advanced topics: providing a graphical view of Bayesian Variable Selection (BVS), recovering sparsity in multivariate nonparametric models and proposing a testing procedure for evaluating nonlinear interaction effect in a semiparametric model. To address the first topic, we propose a new Bayesian variable selection approach via the graphical model and the Ising model, which we refer to the ``Bayesian Ising Graphical Model'' (BIGM). There are several advantages of our BIGM: it is easy to (1) employ the single-site updating and cluster updating algorithm, both of which are suitable for problems with small sample sizes and a larger number of variables, (2) extend this approach to nonparametric regression models, and (3) incorporate graphical prior information. In the second topic, we propose a Nonnegative Garrote on a Kernel machine (NGK) to recover sparsity of input variables in smoothing functions. We model the smoothing function by a least squares kernel machine and construct a nonnegative garrote on the kernel model as the function of the similarity matrix. An efficient coordinate descent/backfitting algorithm is developed. The third topic involves a specific genetic pathway dataset in which the pathways interact with the environmental variables. We propose a semiparametric method to model the pathway-environment interaction. We then employ a restricted likelihood ratio test and a score test to evaluate the main pathway effect and the pathway-environment interaction.
Ph. D.
APA, Harvard, Vancouver, ISO, and other styles
7

Chu, Shuyu. "Change Detection and Analysis of Data with Heterogeneous Structures." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78613.

Full text
Abstract:
Heterogeneous data with different characteristics are ubiquitous in the modern digital world. For example, the observations collected from a process may change on its mean or variance. In numerous applications, data are often of mixed types including both discrete and continuous variables. Heterogeneity also commonly arises in data when underlying models vary across different segments. Besides, the underlying pattern of data may change in different dimensions, such as in time and space. The diversity of heterogeneous data structures makes statistical modeling and analysis challenging. Detection of change-points in heterogeneous data has attracted great attention from a variety of application areas, such as quality control in manufacturing, protest event detection in social science, purchase likelihood prediction in business analytics, and organ state change in the biomedical engineering. However, due to the extraordinary diversity of the heterogeneous data structures and complexity of the underlying dynamic patterns, the change-detection and analysis of such data is quite challenging. This dissertation aims to develop novel statistical modeling methodologies to analyze four types of heterogeneous data and to find change-points efficiently. The proposed approaches have been applied to solve real-world problems and can be potentially applied to a broad range of areas.
Ph. D.
APA, Harvard, Vancouver, ISO, and other styles
8

Lan, Jing. "Gaussian mixture model based system identification and control." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0014640.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Vakil, Sam. "Gaussian mixture model based coding of speech and audio." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=81575.

Full text
Abstract:
The transmission of speech and audio over communication channels has always required speech and audio coders with reasonable search and computational complexity and good performance relative to the corresponding distortion measure.
This work introduces a coding scheme which works in a perceptual auditory domain. The input high dimensional frames of audio and speech are transformed to power spectral domain, using either DFT or MDCT. The log spectral vectors are then transformed to the excitation domain. In the quantizer section the vectors are DCT transformed and decorrelated. This operation gives the possibility of using diagonal covariances in modelling the data. Finally, a GMM based VQ is performed on the vectors.
In the decoder part the inverse operations are done. However, in order to prevent negative power spectrum elements due to inverse perceptual transformation in the decoder, instead of direct inversion, a Nonnegative Least Squares Algorithm has been used to switch back to frequency domain. For the sake of comparison, a reference subband based "Excitation Distortion coder" is implemented and comparing the resulting coded files showed a better performance for the proposed GMM based coder.
APA, Harvard, Vancouver, ISO, and other styles
10

Sadarangani, Nikhil 1979. "An improved Gaussian mixture model algorithm for background subtraction." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87293.

Full text
Abstract:
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
Includes bibliographical references (leaves 71-72).
by Nikhil Sadarangani.
M.Eng.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Gaussian process mixture model"

1

Cheng, Russell. Finite Mixture Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0017.

Full text
Abstract:
Fitting a finite mixture model when the number of components, k, is unknown can be carried out using the maximum likelihood (ML) method though it is non-standard. Two well-known Bayesian Markov chain Monte Carlo (MCMC) methods are reviewed and compared with ML: the reversible jump method and one using an approximating Dirichlet process. Another Bayesian method, to be called MAPIS, is examined that first obtains point estimates for the component parameters by the maximum a posteriori method for different k and then estimates posterior distributions, including that for k, using importance sampling. MAPIS is compared with ML and the MCMC methods. The MCMC methods produce multimodal posterior parameter distributions in overfitted models. This results in the posterior distribution of k being biased towards high k. It is shown that MAPIS does not suffer from this problem. A simple numerical example is discussed.
APA, Harvard, Vancouver, ISO, and other styles
2

Low Choy, Samantha, Justine Murray, Allan James, and Kerrie Mengersen. Combining monitoring data and computer model output in assessing environmental exposure. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.18.

Full text
Abstract:
This article discusses an approach that combines monitoring data and computer model outputs for environmental exposure assessment. It describes the application of Bayesian data fusion methods using spatial Gaussian process models in studies of weekly wet deposition data for 2001 from 120 sites monitored by the US National Atmospheric Deposition Program (NADP) in the eastern United States. The article first provides an overview of environmental computer models, with a focus on the CMAQ (Community Multi-Scale Air Quality) Eta forecast model, before considering some algorithmic and pseudo-statistical approaches in weather prediction. It then reviews current state of the art fusion methods for environmental data analysis and introduces a non-dynamic downscaling approach. The static version of the dynamic spatial model is used to analyse the NADP weekly wet deposition data.
APA, Harvard, Vancouver, ISO, and other styles
3

Majumdar, Satya N. Random growth models. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.38.

Full text
Abstract:
This article discusses the connection between a particular class of growth processes and random matrices. It first provides an overview of growth model, focusing on the TASEP (totally asymmetric simple exclusion process) with parallel updating, before explaining how random matrices appear. It then describes multi-matrix models and line ensembles, noting that for curved initial data the spatial statistics for large time t is identical to the family of largest eigenvalues in a Gaussian Unitary Ensemble (GUE multi-matrix model. It also considers the link between the line ensemble and Brownian motion, and whether this persists on Gaussian Orthogonal Ensemble (GOE) matrices by comparing the line ensembles at fixed position for the flat polynuclear growth model (PNG) and at fixed time for GOE Brownian motions. Finally, it examines (directed) last passage percolation and random tiling in relation to growth models.
APA, Harvard, Vancouver, ISO, and other styles
4

Lattman, Eaton E., Thomas D. Grant, and Edward H. Snell. Shape Reconstructions from Small Angle Scattering Data. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199670871.003.0004.

Full text
Abstract:
This chapter discusses recovering shape or structural information from SAXS data. Key to any such process is the ability to generate a calculated intensity from a model, and to compare this curve with the experimental one. Models for the particle scattering density can be approximated as pure homogenenous geometric shapes. More complex particle surfaces can be represented by spherical harmonics or by a set of close-packed beads. Sometimes structural information is known for components of a particle. Rigid body modeling attempts to rotate and translate structures relative to one another, such that the resulting scattering profile calculated from the model agrees with the experimental SAXS data. More advanced hybrid modelling procedures aim to incorporate as much structural information as is available, including modelling protein dynamics. Solutions may not always contain a homogeneous set of particles. A common case is the presence of two or more conformations of a single particle or a mixture of oligomeric species. The method of singular value decomposition can extract scattering for conformationally distinct species.
APA, Harvard, Vancouver, ISO, and other styles
5

ENGINEERING IN PRACTICE: education, research, and applications. Brazil Publishing, 2022. http://dx.doi.org/10.31012/978-65-257-0020-5.

Full text
Abstract:
This book gathers 10 articles jointly written by students and alumni from the Fluminense Federal University (UFF)’s Production Engineering course, located in the Rio das Ostras Campus, in Rio de Janeiro, and by professors from UFF, from Rio de Janeiro State University, Rio de Janeiro State Institute, Northern Fluminense University, Estácio de Sá University and Cândido Mendes University. The publication is a Material, Maintenance and Environmental Engineering Lab (L3MA) initiative. By offering it to the public, the objective is to spread the scientific research that we are promoting and to encourage ou students and former students to enter the academic and scientific environment, as well as its propagation. Within this book, we compile articles of different subjects in the field of engineering, particularly Production Engineering. Technology and science are present in almost every aspect of life in the contemporary world and the present collection of articles portrays part of this reality. The subjects discussed in this book include active methodologies for engineering education, waste reduction, pipelines’ integrity evaluation, analysis of the chemical process industry, management of solid waste, mathematical model to aid public transport scripting process, variability in coffee packaging process and viability of incorporating ash residues from sugarcane bagasse into a soil-cement mixture.
APA, Harvard, Vancouver, ISO, and other styles
6

Amaral, Mateus Carvalho, Flávio Silva Machado, Luiz Antônio de Oliveira Chaves, Maria Helena Teixeira da Silva, and Vanessa End de Oliveira. https://aeditora.com.br/produto/engenharia-na-pratica-ensino-pesquisa-e-aplicacoes/. Brazil Publishing, 2020. http://dx.doi.org/10.31012/978-65-5861-151-6.

Full text
Abstract:
This book gathers 10 articles written conjointly by students and alumni in the Production Engineering program at Universidade Federal Fluminense (UFF) and also by professors from the UFF, Rio de Janeiro State University, Federal Institute of Rio de Janeiro, State University of Northern Rio de Janeiro, Estácio de Sá University and Cândido Mendes University. This publication is an iniciative of the Materials Engineering, Maintenance and Environment Laboratory (L3MA). By offering it to the public, the objective was to disseminate the scientific research we are conducting and to encourage our students and alumni to enter the world of research and its dissemination. In this book we bring together articles on different subjects in the field of engineering, in particular, Production Engineering. In the contemporary world, technology and science are present in almost all fields of life and the present set of articles portrays a part of this reality. The subjects covered in this book cover topics such as active teaching methodologies, experimental analysis of corrosion processes, assessing the integrity of pipelines, reducing material waste in an industrial environment, analyzing the impacts of a the chemical process industry, alternatives to the use of methanol in the biodiesel manufacturing process, variability in a coffee packaging process, mathematical model to assist the routing process of public transports, solid waste management and viability of incorporating ash residues from sugarcane bagasse into a soil-cement mixture.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Gaussian process mixture model"

1

Nickisch, Hannes, and Carl Edward Rasmussen. "Gaussian Mixture Modeling with Gaussian Process Latent Variable Models." In Lecture Notes in Computer Science, 272–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15986-2_28.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Wang, Jingdong, Jianguo Lee, and Changshui Zhang. "Kernel Trick Embedded Gaussian Mixture Model." In Lecture Notes in Computer Science, 159–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39624-6_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Azam, Muhammad, Basim Alghabashi, and Nizar Bouguila. "Multivariate Bounded Asymmetric Gaussian Mixture Model." In Unsupervised and Semi-Supervised Learning, 61–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23876-6_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Ahmed, Eman, Neamat El Gayar, Amir F. Atiya, and Iman A. El Azab. "Fuzzy Gaussian Process Classification Model." In Lecture Notes in Computer Science, 369–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02611-9_37.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Ahn, Sung Mahn, and Sung Baik. "Minimal RBF Networks by Gaussian Mixture Model." In Lecture Notes in Computer Science, 919–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11538059_95.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Hussain, H., S. H. Salleh, C. M. Ting, A. K. Ariff, I. Kamarulafizam, and R. A. Suraya. "Speaker Verification Using Gaussian Mixture Model (GMM)." In IFMBE Proceedings, 560–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21729-6_140.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Yang, Xi, Kaizhu Huang, and Rui Zhang. "Unsupervised Dimensionality Reduction for Gaussian Mixture Model." In Neural Information Processing, 84–92. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12640-1_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Hufnagel, Heike. "A Generative Gaussian Mixture Statistical Shape Model." In A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis, 27–55. Wiesbaden: Vieweg+Teubner Verlag, 2011. http://dx.doi.org/10.1007/978-3-8348-8600-2_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Palmer, Jason A., Kenneth Kreutz-Delgado, and Scott Makeig. "Super-Gaussian Mixture Source Model for ICA." In Independent Component Analysis and Blind Signal Separation, 854–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11679363_106.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Sun, Mengya. "Pruning Technology Based on Gaussian Mixture Model." In The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, 137–44. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89508-2_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Gaussian process mixture model"

1

Park, Sooho, Yu Huang, Chun Fan Goh, and Kenji Shimada. "Robot Model Learning with Gaussian Process Mixture Model." In 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE). IEEE, 2018. http://dx.doi.org/10.1109/coase.2018.8560452.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Zhang, Jiayuan, Ziqi Zhu, and Jixin Zou. "Supervised Gaussian process latent variable model based on Gaussian mixture model." In 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). IEEE, 2017. http://dx.doi.org/10.1109/spac.2017.8304262.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Dai, Qingyang, and Chunhui Zhao. "Incremental Gaussian Mixture Model for Time-varying Process Monitoring." In 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2020. http://dx.doi.org/10.1109/ddcls49620.2020.9275042.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Xiao, Zhibo, Ma Yao, and Huangang Wang. "Multimode process monitoring using prototype-based Gaussian mixture model." In 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7162727.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Tian, Ying, and Wenli Du. "Initial-Parameter-Criterion Based Gaussian Mixture Model Monitoring Method for Non-Gaussian Process." In 2018 37th Chinese Control Conference (CCC). IEEE, 2018. http://dx.doi.org/10.23919/chicc.2018.8483586.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Zhu, Jinlin, Zhiqiang Ge, and Zhihuan Song. "Distributed Gaussian mixture model for monitoring multimode plant-wide process." In 2016 Chinese Control and Decision Conference (CCDC). IEEE, 2016. http://dx.doi.org/10.1109/ccdc.2016.7532040.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Liu, Yan, Fuli Wang, and Yuqing Chang. "Industrial process operating optimality assessment based on Gaussian mixture model." In 2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 2017. http://dx.doi.org/10.1109/ccdc.2017.7978485.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Varadarajan, Jagannadan, Ramanathan Subramanian, Narendra Ahuja, Pierre Moulin, and Jean-Marc Odobez. "Active Online Anomaly Detection Using Dirichlet Process Mixture Model and Gaussian Process Classification." In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2017. http://dx.doi.org/10.1109/wacv.2017.74.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Wu, Qun, Wenli Du, Feng Qian, and Qingsong Ma. "Process monitoring with global probability boundary-based on Gaussian mixture model." In 2013 10th IEEE International Conference on Control and Automation (ICCA). IEEE, 2013. http://dx.doi.org/10.1109/icca.2013.6565031.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Stachniss, Cyrill, Christian Plagemann, Achim Lilienthal, and Wolfram Burgard. "Gas Distribution Modeling using Sparse Gaussian Process Mixture Models." In Robotics: Science and Systems 2008. Robotics: Science and Systems Foundation, 2008. http://dx.doi.org/10.15607/rss.2008.iv.040.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Gaussian process mixture model"

1

De Leon, Phillip L., and Richard D. McClanahan. Efficient speaker verification using Gaussian mixture model component clustering. Office of Scientific and Technical Information (OSTI), April 2012. http://dx.doi.org/10.2172/1039402.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Liu, Nian, and Matthew Sweeney. Gaussian Process Emulators for Volcanic Ash Dispersion Model Tephra2. Office of Scientific and Technical Information (OSTI), July 2022. http://dx.doi.org/10.2172/1879348.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Helmut, Harbrecht, John Davis Jakeman, and Peter Zaspel. Weighted greedy-optimal design of computer experiments for kernel-based and Gaussian process model emulation and calibration. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1608084.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Ramakrishnan, Aravind, Ashraf Alrajhi, Egemen Okte, Hasan Ozer, and Imad Al-Qadi. Truck-Platooning Impacts on Flexible Pavements: Experimental and Mechanistic Approaches. Illinois Center for Transportation, November 2021. http://dx.doi.org/10.36501/0197-9191/21-038.

Full text
Abstract:
Truck platoons are expected to improve safety and reduce fuel consumption. However, their use is projected to accelerate pavement damage due to channelized-load application (lack of wander) and potentially reduced duration between truck-loading applications (reduced rest period). The effect of wander on pavement damage is well documented, while relatively few studies are available on the effect of rest period on pavement permanent deformation. Therefore, the main objective of this study was to quantify the impact of rest period theoretically, using a numerical method, and experimentally, using laboratory testing. A 3-D finite-element (FE) pavement model was developed and run to quantify the effect of rest period. Strain recovery and accumulation were predicted by fitting Gaussian mixture models to the strain values computed from the FE model. The effect of rest period was found to be insignificant for truck spacing greater than 10 ft. An experimental program was conducted, and several asphalt concrete (AC) mixes were considered at various stress levels, temperatures, and rest periods. Test results showed that AC deformation increased with rest period, irrespective of AC-mix type, stress level, and/or temperature. This observation was attributed to a well-documented hardening–relaxation mechanism, which occurs during AC plastic deformation. Hence, experimental and FE-model results are conflicting due to modeling AC as a viscoelastic and the difference in the loading mechanism. A shift model was developed by extending the time–temperature superposition concept to incorporate rest period, using the experimental data. The shift factors were used to compute the equivalent number of cycles for various platoon scenarios (truck spacings or rest period). The shift model was implemented in AASHTOware pavement mechanic–empirical design (PMED) guidelines for the calculation of rutting using equivalent number of cycles.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Gaussian process mixture model' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography