PPT-The Magic of Random Sampling:

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. RAN#. 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. 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. 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. 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. Sampling . using. RANDOM. Random Sampling using RANDOM. Random: 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. Richard Peng. M.I.T.. Joint work with . Dehua. Cheng, Yu Cheng, Yan Liu and . Shanghua. . Teng. (U.S.C.). Outline. Gaussian sampling, linear systems, matrix-roots. Sparse factorizations of . L. p. Richard Peng. M.I.T.. OUtline. Structure preserving sampling. Sampling as a recursive ‘driver’. Sampling the inaccessible. What can sampling preserve?. Random Sampling. Collection of many objects. 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. Richard Peng. M.I.T.. Joint work with . Dehua. Cheng, Yu Cheng, Yan Liu and . Shanghua. . Teng. (U.S.C.). Outline. Gaussian sampling, linear systems, matrix-roots. Sparse factorizations of . L. p. 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.