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IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019(2)
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scopus(8)
A data augmentation approach for a class of statistical inference problems
ArticleAbstract: We present an algorithm for a class of statistical inference problems. The main idea is to reformulaPalabras claves:Autores:Carvajal R., Escarate P., Juan C. Agüero, Katselis D., Orellana R.Fuentes:scopusA method to deconvolve stellar rotational velocities III. The probability distribution function via maximum likelihood utilizing finite distribution mixtures
ArticleAbstract: Aims. The study of accurate methods to estimate the distribution of stellar rotational velocities isPalabras claves:Methods: analytical, methods: data analysis, methods: numerical, Methods: statistical, Stars: fundamental parameters, Stars: rotationAutores:Carvajal R., Christen A., Curé M., Escarate P., Juan C. Agüero, Orellana R.Fuentes:scopusEmpirical Bayes estimation utilizing finite Gaussian Mixture Models
Conference ObjectAbstract: In this paper we develop an identification algorithm to obtain an estimation of the prior distributiPalabras claves:Bayesian inference, Empirical Bayes, ExpectationMaximization, Gaussian Mixture, Prior distributionAutores:Carvajal R., Juan C. Agüero, Orellana R.Fuentes:scopusOn the uncertainty identification for linear dynamic systems using stochastic embedding approach with gaussian mixture models
ArticleAbstract: In control and monitoring of manufacturing processes, it is key to understand model uncertainty in oPalabras claves:Expectation-maximization, Gaussian Mixture Model, Maximum likelihood, Stochastic embedding, Uncertainty modelingAutores:Carvajal R., Escarate P., Juan C. Agüero, Orellana R.Fuentes:scopusOn the uncertainty modelling for linear continuous-time systems utilising sampled data and Gaussian mixture models
Conference ObjectAbstract: In this paper a Maximum Likelihood estimation algorithm for model error modelling in a continuous-tiPalabras claves:continuous-time model, Discrete-time model, Gaussian Mixture Model, Maximum likelihood, Stochastic embeddingAutores:Carvajal R., Coronel M., Delgado R.A., Escarate P., Juan C. Agüero, Orellana R.Fuentes:scopusMaximum Likelihood estimation for non-minimum-phase noise transfer function with Gaussian mixture noise distribution
ArticleAbstract: In this paper a Maximum Likelihood estimation algorithm for a linear dynamic system driven by an exoPalabras claves:Expectation–Maximization, Gaussian mixture noise distribution, Maximum likelihood, Non-minimum-phase transfer functionAutores:Bittner G., Carvajal R., Juan C. Agüero, Orellana R.Fuentes:scopusMaximum Likelihood identification for Linear Dynamic Systems with finite Gaussian mixture noise distribution
Conference ObjectAbstract: This paper considers the identification of a linear dynamic system driven by a non-Gaussian noise diPalabras claves:Gaussian Mixture Model, Linear Dynamical Systems, Maximum likelihood, Non-Gaussian Noise DistributionAutores:Bittner G., Carvajal R., Juan C. Agüero, Orellana R.Fuentes:scopusModel error modelling using a stochastic embedding approach with gaussian mixture models for FIR systems
Conference ObjectAbstract: In this paper a Maximum Likelihood estimation algorithm for error-model modelling using a stochasticPalabras claves:Estimation, Expectation-maximization, Gaussian Mixture, Maximum likelihood, Model errors, Stochastic embeddingAutores:Carvajal R., Goodwin G.C., Juan C. Agüero, Orellana R.Fuentes:scopus