PPT-RANDOM SAMPLING

PRACTICAL APPLICATIONS IN eDISCOVERY WELCOME Thank you for joining Numerous diverse attendees Todays topic and presenters Question submission for later response You will receive slides recording and survey tomorrow . Basic Terms. Research units – subjects, participants. Population of . interest (all humans?). Accessible . population – those you can actually try to sample. Intended . sample – those you select for participation. Population = group of people you need to know information about. Census = information obtained from every person in population. Sample = small group from within the population. Sample survey = investigation done using a sample. Anup. Bhattacharya. IIT Delhi. . Joint work with Davis . Issac. (MPI), . Ragesh. . Jaiswal. (IITD) and Amit Kumar (IITD). Introduction: Sampling. Select a subset of data. Computations on “representative” subset would approximate computations on whole data. It is important that the sample selected be representative of the population from which it is taken so that inferences about the population are the best that they can be.. Probability Sampling Methods. Yu Su*, Gagan Agrawal*, . Jonathan Woodring. #. Kary Myers. #. , Joanne Wendelberger. #. , James Ahrens. #. *The Ohio . State University. #. Los . Alamos National . Laboratory. Motivation. Science becomes increasingly data driven;. Sampling is perhaps the most important step in assuring that good quality aggregates are being used on INDOT contracts. Since a sample is just a small portion of the total material, the importance th SAI India. September 2011. Sampling is used by SAI-India extensively in. Financial Audit. Compliance Audit. Performance Audit. Sampling. Planning. – selection of units for audit. Audit Execution – selection of transactions for detailed scrutiny. From Surveys to Big . D. ata. Edith Cohen. Google Research. Tel Aviv University. Disclaimer:. Random sampling is classic and well studied tool with enormous impact across disciplines. This presentation is biased and limited by its length, my research interests, experience, understanding, and being a Computer Scientist. I will attempt to present some big ideas and selected applications. I hope to increase your appreciation of this incredible tool.. How . can it be that mathematics, being after all a product of human thought independent of experience, is so admirably adapted to the objects . of reality. Albert Einstein. Some parts of these slides were prepared based on . Random Sampling using Ran#. The Ran#: Generates . a pseudo . random number to 3 decimal places that . is less than 1.. i.e. . it generates a random number in the range . [0, 1. ]. . Ran#. . is in Yellow. How . can it be that mathematics, being after all a product of human thought independent of experience, is so admirably adapted to the objects . of reality. Albert Einstein. Some parts of these slides were prepared based on . 7. Introduction. In . a typical statistical inference problem, you want to discover one or more characteristics of a given population. .. However, it is generally difficult or even impossible to contact each member of the population.. Ke. Yi. Hong Kong University of Science and Technology. yike@ust.hk. Random Sampling on Big Data. 2. “Big Data” in one slide. The 3 V’s. : . Volume. External memory algorithms. Distributed data. Random Sampling using Ranint for an interval [1,200]. . Ranint. . is in Red. Random Sampling using Ranint for an interval [1,200]. Random Sampling using Ranint for an interval [1,200]. We want our interval to be.