搜索结果: 1-15 共查到“理论统计学 Methods”相关记录49条 . 查询时间(0.218 秒)
First order optimization methods often perform poorly on ill-conditioned optimization problems. However, by preconditioning the problem data and solving the preconditioned problem, the performance of ...
On the Validity of Long-Run Estimation Methods for Discrete-Event Systems
Validity Long-Run Estimation Methods Discrete-Event Systems
2015/7/8
On the Validity of Long-Run Estimation Methods for Discrete-Event Systems.
Comparing composite likelihood methods based on pairs for spatial Gaussian random fieldsM
Covariance estimation Geostatistics Large datasets Tapering
2013/6/14
In the last years there has been a growing interest in proposing methods for estimating covariance functions for geostatistical data. Among these, maximum likelihood estimators have nice features when...
Methods to Calculate the Upper Bound of Gini Coefficient Based on Grouped Data and the Result for China
Gini coefficient Grouped data Upper bound China
2013/6/14
How to give an upper bound, especially the smallest upper bound of Gini coefficient based on grouped data in the absence of income brackets is still a problem not properly solved. This article provide...
On the Complexity Analysis of Randomized Block-Coordinate Descent Methods
Randomized block-coordinate descent accelerated coordinate descent iteration complexity convergence rate composite minimization
2013/6/17
In this paper we analyze the randomized block-coordinate descent (RBCD) methods proposed in [8,11] for minimizing the sum of a smooth convex function and a block-separable convex function. In particul...
Fourier methods for smooth distribution function estimation
Fourier analysis kernel distribution estimation mean integrated squared error optimal bandwidth sinc kernel
2013/6/14
In this paper we show how to use Fourier transform methods to analyze the asymptotic behavior of kernel distribution function estimators. Exact expressions for the mean integrated squared error in ter...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
Two General Methods for Population Pharmacokinetic Modeling: Non-Parametric Adaptive Grid and Non-Parametric Bayesian
Population pharmacokinetic modeling non-parametric maximum likelihood Bayesian Stick-breaking Pmetrics RJags
2013/5/2
Population pharmacokinetic (PK) modeling methods can be statistically classified as either parametric or nonparametric (NP). Each classification can be divided into maximum likelihood (ML) or Bayesian...
Generalized Sobol sensitivity indices for dependent variables: numerical methods
Dependent variables Extended basis Greedy algorithm LARS Sensitivity analysis Sobol decomposition
2013/4/28
The hierarchically orthogonal functional decomposition of any measurable function f of a random vector X=(X_1,...,X_p) consists in decomposing f(X) into a sum of increasing dimension functions dependi...
Graphical methods for inequality constraints in marginalized DAGs
Graphical methods inequality constraints marginalized DAGs
2012/11/22
We present a graphical approach to deriving inequality constraints for directed acyclic graph (DAG) models, where some variables are unobserved. In particular we show that the observed distribution of...
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
Stochastic Dual Coordinate Ascent Methods Regularized Loss Minimization
2012/11/22
Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees. While the closel...
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
Stochastic Dual Coordinate Ascent Methods Regularized Loss Minimization
2012/11/22
Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees. While the closel...
Iteration Complexity of Randomized Block-Coordinate Descent Methods for Minimizing a Composite Function
Block coordinate descent iteration complexity composite minimization
2011/7/19
In this paper we develop a randomized block-coordinate descent method for minimizing the sum of a smooth and a simple nonsmooth block-separable convex function and prove that it obtains an $\epsilon$-...
Spectral Methods for Learning Multivariate Latent Tree Structure
Multivariate Latent Spectral Methods
2011/7/19
This work considers the problem of learning the structure of a broad class of multivariate latent variable tree models, which include a variety of continuous and discrete models (including the widely ...