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昆明理工大学理学院概率论与数理统计课件Chapter 6 Random Sampling--Sampling Distributions
昆明理工大学理学院 概率论与数理统计 课件 Chapter 6 Random Sampling Sampling Distributions
2017/4/17
昆明理工大学理学院概率论与数理统计课件Chapter 6 Random Sampling--Sampling Distributions.
昆明理工大学理学院概率论与数理统计课件Chapter 6 Random Sampling--Some Important Statistics
昆明理工大学理学院 概率论与数理统计 课件 Chapter 6 Random Sampling Some Important Statistics
2017/4/17
昆明理工大学理学院概率论与数理统计课件Chapter 6 Random Sampling--Some Important Statistics.
昆明理工大学理学院概率论与数理统计课件Chapter 6 Random Sampling--Random Sampling
昆明理工大学理学院 概率论与数理统计 课件 Chapter 6 Random Sampling Random Sampling
2017/4/17
昆明理工大学理学院概率论与数理统计课件Chapter 6 Random Sampling--Random Sampling.
A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning
Inferring Label Sampling Mechanisms Semi-Supervised Learning
2015/8/21
We consider the situation in semi-supervised learning, where the “label sampling” mechanism stochastically depends on the true response (as well as potentially on the features). We suggest a method of...
We propose a sketch-based sampling algorithm, which effectively exploits the data sparsity. Sampling methods have become popular in large-scale data mining and information retrieval, where high data s...
One Sketch For All:Theory and Application of Conditional Random Sampling
One Sketch For All Theory and Application Conditional Random Sampling
2015/8/21
Conditional Random Sampling (CRS) was originally proposed for efficiently computing pairwise (l2, l1) distances, in static, large-scale, and sparse data. This study modifies the original CRS and exten...
ALGEBRAIC ALGORITHMS FOR SAMPLING FROM CONDITIONAL DISTRIBUTIONS
Structure markov chain algorithm the discrete sampling statistical index family conditions spectral analysis data
2015/7/14
We construct Markov chain algorithms for sampling from discrete
exponential families conditional on a sufticient statistic. Examples include
contingency tables, logistic regression, and spectral a...
Estimating Hidden Population Size using Respondent-Driven Sampling Data
Respondent-Driven Sampling Data Estimating Hidden Population Size
2012/11/23
Respondent-Driven Sampling (RDS) is an approach to sampling design and inference in hard-to-reach human populations. Typically, a sampling frame is not available, and population members are difficult ...
High-frequency sampling and kernel estimation for continuous-time moving average processes
CARMA process continuous-time moving average process discretely sampled process FICARMA process gamma kernel
2011/9/19
Abstract: Interest in continuous-time processes has increased rapidly in recent years, largely because of the high-frequency data available in many areas of application, particularly in finance and tu...
Ratio Estimators in Simple Random Sampling when Study Variable is an Attribute
Ratio Estimators Simple Random Sampling
2010/11/9
In this paper we have suggested a family of estimators for the population mean when study variable itself is qualitative in nature. Expressions for the bias and mean square error (MSE) of the suggest...
Estimation of a probability in inverse binomial sampling under normalized linear-linear and inverse-linear loss
Sequential estimation Point estimator Inverse binomial sampling
2010/12/3
Sequential estimation of the success probability p in inverse binomial sam-pling is considered in this paper. For any estimator ˆp, its quality is measured by the risk associated with normalized ...
Path sampling for particle filters with application to multi-target tracking
Path sampling particle filters with application multi-target tracking
2010/12/3
In recent work [15], we have presented a novel approach for improving particle filters for multi-target tracking. The suggested approach was based on Girsanov’s change of measure theorem for stochasti...
Sampling and Recovery of Multidimensional Bandlimited Functions via Frames
Sampling and Recovery Multidimensional Bandlimited Functions
2010/12/3
In this paper, we investigate frames for L2[−, ]d consisting of exponential functions in connection to oversampling and nonuniform sampling of bandlimited func-tions.