Random sampling and greedy sparsification for matroid optimization problems

1998 ◽  
Vol 82 (1-2) ◽  
pp. 41-81 ◽  
Author(s):  
David R. Karger
Keyword(s):  
Random Sampling ◽  
Optimization Problems ◽  
Matroid Optimization

Random sampling

2019 ◽  
pp. 71-92
Author(s):  
Max A. Little
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Statistical Model ◽  
Random Sampling ◽  
Joint Distribution ◽  
Optimization Problems ◽  
Viable Alternative ◽  
Statistical Machine Learning ◽  
Digital Signals ◽  
Large Numbers

This chapter provides an overview of generating samples from random variables with a given (joint) distribution, and using these samples to find quantities of interest from digital signals. This task plays a fundamental role in many problems in statistical machine learning and signal processing. For example, effectively simulating the behaviour of the statistical model offers a viable alternative to optimization problems arising from some models for signals with large numbers of variables.

scholarly journals Evolutionary Algorithms and Matroid Optimization Problems

Algorithmica ◽  
2008 ◽  
Vol 57 (1) ◽  
pp. 187-206 ◽  
Author(s):  
Joachim Reichel ◽  
Martin Skutella
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Matroid Optimization

Selective Sampling and Orientation of Non-Homogeneous Tissues

1968 ◽  
Vol 26 ◽  
pp. 98-99
Author(s):  
C. C. Clawson ◽  
L. W. Anderson ◽  
R. A. Good
Keyword(s):  
Electron Microscopy ◽  
Electron Microscope ◽  
Light Microscopy ◽  
Random Sampling ◽  
New Method ◽  
Microscope Examination ◽  
Small Areas ◽  
Selective Sampling ◽  
Large Numbers ◽  
Specific Orientation

Investigations which require electron microscope examination of a few specific areas of non-homogeneous tissues make random sampling of small blocks an inefficient and unrewarding procedure. Therefore, several investigators have devised methods which allow obtaining sample blocks for electron microscopy from region of tissue previously identified by light microscopy of present here techniques which make possible: 1) sampling tissue for electron microscopy from selected areas previously identified by light microscopy of relatively large pieces of tissue; 2) dehydration and embedding large numbers of individually identified blocks while keeping each one separate; 3) a new method of maintaining specific orientation of blocks during embedding; 4) special light microscopic staining or fluorescent procedures and electron microscopy on immediately adjacent small areas of tissue.

Faktor-Faktor Yang Mempengaruhi Keputusan Mahasiswa Memilih Program Studi Kebidanan STIKES Muhammadiyah Ciamis Tahun 2016

2018 ◽  
Vol 5 (1) ◽  
pp. 69-82
Author(s):  
Evi Mariana
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Random Sampling ◽  
Sampling Method ◽  
Marketing Mix ◽  
Stratified Random Sampling ◽  
Price Promotion ◽  
F Test ◽  
Physical Evidence

The purpose of this study was to analyze the factors that influence the decisionof the students chose to study in Obstetrics Prodi STIKES Muhammadiyah Ciamis and analyze the factors that most influence the decision of the students chose to study in Obstetrics Prodi STIKES Muhammadiyah Ciamis. Collecting data in this study was conducted using a survey by questionnaire to 114 students by stratified random sampling method. Methods of data analysis using multiple linear regression, F test and test T. The result is a marketing mix that significantly is the product, place, and physical evidence. And that does not affect the marketing mix is price, promotion, place, and processes

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