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Found 9 papers, 0 papers with code
Model Bridging: Connection between Simulation Model and Neural Network
One approach is replacing the un-interpretable machine learning model with a surrogate model, which has a simple structure for interpretation.
Simulator Calibration under Covariate Shift with Kernels
We propose a novel calibration method for computer simulators, dealing with the problem of covariate shift.
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
We propose a novel approach to parameter estimation for simulator-based statistical models with intractable likelihood.
Effects of Additional Data on Bayesian Clustering
The present paper presents a theoretical analysis of the accuracy of such a model and clarifies which factor has the greatest effect on its accuracy, the advantages of obtaining additional data, and the disadvantages of increasing the complexity.
Bayesian Estimation of Multidimensional Latent Variables and Its Asymptotic Accuracy
A previous study proposed a method that can be used when the range of the latent variable is redundant compared with the model generating data.
Asymptotic Accuracy of Bayesian Estimation for a Single Latent Variable
In a previous study, we determined the accuracy of a Bayes estimation for the joint probability of the latent variables in a dataset, and we proved that the Bayes method is asymptotically more accurate than the maximum-likelihood method.
Accuracy of Latent-Variable Estimation in Bayesian Semi-Supervised Learning
Unsupervised learning tasks, such as cluster analysis, are regarded as estimations of latent variables based on the observable ones.
Asymptotic Accuracy of Bayes Estimation for Latent Variables with Redundancy
In the singular case, on the other hand, the models are not identifiable and the Fisher matrix is not positive definite.
Asymptotic Accuracy of Distribution-Based Estimation for Latent Variables
Hierarchical statistical models are widely employed in information science and data engineering.
