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Bayesian regression and classification using mixtures of Gaussian processes
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond
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Hierarchical Gaussian process mixtures for regression
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Mixtures of Gaussian process priors
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Analytic framework for blended multiple model systems using linear local models
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On the interpretation of local models in blended multiple model structures
inferredcites →https://doi.org/10.1109/34.735807openalex-snapshot
Bayesian classification with Gaussian processes
inferredcites →https://doi.org/10.1109/91.855918openalex-snapshot
On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models
inferredcites →https://doi.org/10.1111/1467-9868.00362openalex-snapshot
Modelling Spatially Correlated Data via Mixtures: A Bayesian Approach
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Curve Fitting and Optimal Design for Prediction
inferredcites →https://doi.org/10.2307/2289504openalex-snapshot
Statistical Analysis of Finite Mixture Distributions.
inferredcites →https://doi.org/10.2307/2347565openalex-snapshot
Adaptive Rejection Sampling for Gibbs Sampling
inferredcites →https://doi.org/10.2307/2531224openalex-snapshot
Statistical Analysis of Finite Mixture Distributions.
inferredcites →https://doi.org/10.23919/ecc.1999.7099884openalex-snapshot
On transient dynamics, off-equilibrium behaviour and identification in blended multiple model structures
inferredcites →https://doi.org/10.3182/20020721-6-es-1901.00672openalex-snapshot
ON MULTI-OBJECTIVE IDENTIFICATION OF TAKAGI-SUGENO FUZZY MODEL PARAMETERS
inferredcites →https://doi.org/10.48550/arxiv.physics/9701026openalex-snapshot
Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification
inferredcites →https://doi.org/10.7551/mitpress/1120.001.0001openalex-snapshot
Advances in Neural Information Processing Systems 14
inferredcites →https://doi.org/10.7551/mitpress/11474.003.0014openalex-snapshot
Neural Information Processing Systems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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A Specialized Probability Density Function for the Input of Mixture of Gaussian Processes
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Automatic Model Selection of the Mixtures of Gaussian Processes for Regression
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A DAEM Algorithm for Mixtures of Gaussian Process Functional Regressions
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Classification Using Mixture of Discriminative Learners: The Case of Compositional Data
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Self-tuning Control of Non-linear Systems Using Gaussian Process Prior Models
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An Efficient EM Approach to Parameter Learning of the Mixture of Gaussian Processes
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Stochastic variational hierarchical mixture of sparse Gaussian processes for regression
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Variational Bayesian multinomial logistic Gaussian process classification
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Curve prediction and clustering with mixtures of Gaussian process functional regression models
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Comprehensive utilization of solid waste resources: Development of wet shotcrete for mines
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Exploiting Hessian matrix and trust-region algorithm in hyperparameters estimation of Gaussian process
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Response surface methodology with prediction uncertainty: A multi-objective optimisation approach
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Quantitative analysis of yeast growth process based on FT-NIR spectroscopy integrated with Gaussian mixture regression
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Approximate implementation of the logarithm of the matrix determinant in Gaussian process regression
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Dynamic systems identification with Gaussian processes
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Forecasting the evolution of nonlinear and nonstationary systems using recurrence-based local Gaussian process models
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Nonparametric Bayesian supervised classification of functional data
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Scalable Hierarchical Mixture of Gaussian Processes for Pattern Classification
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