搜索结果: 151-165 共查到“知识库 科学技术统计学”相关记录243条 . 查询时间(3.307 秒)
This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such p...
Fused Multiple Graphical Lasso
Fused Multiple Graphical Lasso
2012/11/23
In this paper, we consider the problem of estimating multiple graphical models simultaneously using the fused lasso penalty, which encourages adjacent graphs to share similar structures. A motivating ...
The question of aggregating pairwise comparisons to obtain a global ranking over a collection of objects has been of interest for a very long time: be it ranking of online gamers (e.g. MSR's TrueSkill...
论社会科技统计发展的基本思路
社会科技统计 社会发展 科技事业发展
2013/8/22
社会科技统计是社会发展和科技事业发展的重要基础工作，是整个统计体系的重要组成部分，其工作状态与水平如何，是衡量一个国家和地区统计工作的重要尺度，亦是侧面反映一个国家和地区社会科技发展水平的重要标志。但是由于历史的原因，中国社会科技统计起步较晚，基础差，底子薄。按照加快实现两个根本性转变和科教兴国战略对社会科技统计工作提出的要求衡量，社会科技统计明显滞后于社会科技发展的需要，是整个统计工作中最为薄弱...
Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utility-based framework...
A Sequence of Relaxations Constraining Hidden Variable Models
Sequence Relaxations Constraining Hidden Variable Models
2011/7/6
Many widely studied graphical models with latent variables lead to nontrivial constraints on the distribution of the observed variables. Inspired by the Bell inequalities in quantum mechanics, we refe...
On false discovery rate thresholding for classification under sparsity
false discovery rate thresholding classification under sparsity
2011/7/6
We study the properties of false discovery rate (FDR) thresholding, viewed as a classification procedure. The "0"-class (null) is assumed to have a known, symmetric log-concave density while the "1"-c...
Distributional Results for Thresholding Estimators in High-Dimensional Gaussian Regression Models
Markov chain Monte Carlo Hamiltonian dynamics Bayesian analysis
2011/7/6
We study the distribution of hard-, soft-, and adaptive soft-thresholding estimators within a linear regression model where the number of parameters k can depend on sample size n and may diverge with ...
Split Hamiltonian Monte Carlo
Markov chain Monte Carlo Hamiltonian dynamics Bayesian analysis
2011/7/6
We show how the Hamiltonian Monte Carlo algorithm can sometimes be speeded up by "splitting" the Hamiltonian in a way that allows much of the movement around the state space to be done at low computat...
Nonparametric Estimation of Second-Order Jump-Diffusion Model
Second-order jump-diffusion N-W estimator Weak consistency
2011/7/6
We study the nonparametric estimators of the infinitesimal coefficients of the second-order jump-diffusion models. Under the mild conditions, we obtain the weak consistency and the asymptotic normalit...
Balls in Boxes: Variations on a Theme of Warren Ewens and Herbert Wilf
Balls in Boxes Variations on a Theme Warren Ewens Herbert Wilf
2011/7/6
We comment on, elaborate, and extend the work of Warren Ewens and Herbert Wilf, described in their this http URL about the maximum in balls-and-boxes problem.
High-dimensional additive hazard models and the Lasso
Survival analysis Counting processes Censored data
2011/7/6
We consider a general high-dimensional additive hazard model in a non-asymptotic setting, including regression for censored-data.
This paper presents Natural Evolution Strategies (NES), a recent family of algorithms that constitute a more principled approach to black-box optimization than established evolutionary algorithms.
Cooperative spectrum sensing over unreliable reporting channel
cognitive radio cooperation spectrum sensing data fusion
2011/7/6
This article aims to analyze a cooperative spectrum sensing scheme using a centralized approach with unreliable reporting channel. The spectrum sensing is applied to a cognitive radio system, where ea...
Uniform Stability of a Particle Approximation of the Optimal Filter Derivative
Hidden Markov Models State-Space Models Sequential Monte Carlo
2011/7/5
Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models.