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Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Direct sampling method to inverse wave-number-dependent source problems: determination of the support of a stationary source
反波数 相关源问题 直接采样 固定源
2023/11/29
This talk is concerned with a direct sampling method for imaging the support of a frequency-dependent source term embedded in a homogeneous and isotropic medium. The source term is given by the Fourie...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Deep adaptive sampling for numerical PDEs
数值 偏微分方程 深度 自适应采样
2023/5/15
Adaptive computation is of great importance in numerical simulations. The ideas for adaptive computations can be dated back to adaptive finite element methods in 1970s. In this talk, we shall first re...
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.
OPTIMAL BEAM PATTERN DESIGN FOR VERY LARGE SENSOR ARRAYS WITH SPARSE SAMPLING
OPTIMAL BEAM PATTERN DESIGN VERY LARGE SENSOR ARRAYS SPARSE SAMPLING
2015/9/29
Consider a large scale sensor array having N sensors that monitors a surveillance area. Using all sensors simultaneously may be unreasonable in terms of power consumption and data processing.For examp...
Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling
Hidden Population Sampling
2015/9/11
Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling。
EXTENSIONS OF RESPONDENT-DRIVEN SAMPLING: ANALYZING CONTINUOUS VARIABLES AND CONTROLLING FOR DIFFERENTIAL RECRUITMENT
RESPONDENT-DRIVEN SAMPLING DIFFERENTIAL RECRUITMENT
2015/9/11
Respondent-driven sampling (RDS) is a network-based method
for sampling hidden and hard-to-reach populations that has been
shown to produce asymptotically unbiased population estimates
when its ass...
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...
Discussion of: \Sequential Quasi-Monte-Carlo Sampling" by Mathieu Gerber and Nicolas Chopin
Mathieu Gerber Nicolas Chopin
2015/8/21
We congratulate Gerber and Chopin for a very interesting paper with much
promise for applications. SQMC is similar to array-RQMC (L'Ecuyer et al.,
2008), in using T sets of N points in [0;1]d
inste...
Optimal mixture weights in multiple importance sampling
multiple importance sampling mixture
2015/8/21
In multiple importance sampling we combine samples from a nite list
of proposal distributions. When those proposal distributions are used to
create control variates, it is possible (Owen and Zhou, ...
Other space- lling
curves, such as those due to Sierpinski and Peano, also attain these
rates, while upper bounds for the Lebesgue curve are somewhat worse,
as if the dimension were log2
(3) times...
Guaranteed Conservative Fixed Width Confidence Intervals Via Monte Carlo Sampling
Intervals Via Monte Carlo Sampling Guaranteed Conservative
2015/8/21
Monte Carlo methods are used to approximate the means, μ, of random variables
Y, whose distributions are not known explicitly. The key idea is that the average of a random
sample, Y1,...,Yn, tends t...