Parameter learning for linear Gaussian model
DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model
... The original ICA-LiNGAM algorithm has several potential problems: i) Most ICA algorithms in- cluding FastICA (Hyv¨arinen, 1999) and gradient-based algorithms (Amari, 1998) may not converge to a correct solution in a ...
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Parameter estimation and inference in the linear mixed model
... the parameter space, for example testing H 0 : σ a 2 = 0 against H A : σ a 2 > 0, where σ a 2 is the random effects variance, the standard asymptotic theory no longer holds, as regularity conditions are not ...
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A Linear Non-Gaussian Acyclic Model for Causal Discovery
... is linear, (b) there are no unobserved confounders, and (c) disturbance variables have non-Gaussian distributions of non-zero ...for Linear Non-Gaussian Acyclic Model), and demonstrate ...
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Parameter-robust linear quadratic Gaussian technique for multi-agent slung load transportation
... Continuous mass variation (b) PRLQG Figure 12: Effect of payload variation in 4-UAV transportation system other previous methods, resulting in smaller size of matrix inversion. Therefore, the proposed method is expected ...
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Parameter identification of a linear single track vehicle model
... bicycle model. For that, a first simple model will be derived from the equations of motion of the bicycle ...enhanced model taking into account tyre relaxation ...This model will also enable ...
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Model predictive and linear quadratic Gaussian control of a wind turbine
... Received . . . KEY WORDS: Wind turbine control, Model Predictive Control, Linear Quadratic Gaussian, observer 1. INTRODUCTION There is much interest in renewable energy due to concern over the ...
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On the Bayesian treed multivariate Gaussian process with linear model of coregionalization.
... can model only a special case of non-stationarity since it does not allow for the spatial correlation to vary on ...multivariate model based on the Bayesian treed multivariate Gaussian process ...
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Convergence of Gaussian Belief Propagation Under General Pairwise Factorization: Connecting Gaussian MRF with Pairwise Linear Gaussian Model
... joint Gaussian distribution. However, Gaussian BP is only guaranteed to converge in singly connected graphs and may fail to converge in loopy ...of Gaussian BP are all tailored for one par- ticular ...
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Hyper parameter Optimisation of Gaussian Process Reinforcement Learning for Statistical Dialogue Management
... Abstract Gaussian processes reinforcement learn- ing provides an appealing framework for training the dialogue policy as it takes into account correlations of the objec- tive function given different dialogue be- ...
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Parameter Tuning Using Gaussian Processes
... tree learning (J48) for example: GPO shows statistically significant improvements in accuracy in some cases, such as for the “auto” and “audiology” datasets, no matter how many points are evaluated in Random ...
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A new two-parameter estimator for the inverse Gaussian regression model with application in chemometrics
... The presence of multicollinearity among the explanatory variables has un- desirable effects on the maximum likelihood estimator (MLE). The inverse Gaussian regression (IGR) model is a well-known ...
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Variational Bayesian Parameter Estimation Techniques for the General Linear Model
... unknown, model parameters, be it the point-estimates of classical frequentist statistics or the posterior distributions of the Bayesian paradigm ...unknown, model parameters and model ...both ...
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Learning Sparse Gaussian Graphical Model with l0-regularization
... of learning sparse Gaussian graphical models, it is desirable to obtain both sparse structures as well as good parameter es- ...on parameter estima- tion or constrain to specific ...
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Application of Mean-Square Approximation for Piecewise Linear Optimal Compander Design for Gaussian Source and Gaussian Mixture Model
... quantizer model can be the minimization of this loss, ...quantizer model properties usually begins with the consideration of the properties of the most common types of scalar quantizers, uniform and ...
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Causal Structure Learning and Effect Identification in Linear Non-Gaussian Models and Beyond
... The preferred approach to causal inference is to carry out controlled exper- iments. However, such experiments are not always possible due to ethical, financial or technical restrictions. An important problem is thus the ...
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Estimation of the slope parameter for linear regression model with uncertain prior information
... may well have some superior statistical property in terms of another more popular statistical criterion, namely the mean square error. In this process, we define three biased estimators: the restricted estimator (RE) ...
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Estimation of the slope parameter for linear regression model with uncertain prior information
... may well have some superior statistical property in terms of another more popular statistical criterion, namely the mean square error. In this process, we define three biased estimators: the restricted estimator (RE) ...
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Heat Transfer Analysis of Linear Compressor Based on a Lumped Parameter Model
... a linear compressor was analyzed by lumped parameter ...The linear compressor has a more complicated suction structure than the conventional compressor because the piston acts as a suction ...in ...
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UAV Parameter Estimation with Gaussian Process Approximations
... behind Gaussian processes is presented. Chapter 3 presents Gaussian process modeling of the flight ...Dependent Gaussian Processes (DGPs) to learn an aerodynamic ...for parameter ...
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Linear Regression Model for Gaussian Noise Estimation and Removal for Medical Ultrasound Images
... novel linear regression model for Gaussian representation of speckle noise in medical ultrasound ...a Gaussian noise, with estimated mean and standard deviation based on PSNR of the ultrasound ...
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