PPT-Random Sampling

and Introduction to Experimental Design Simple Random Sample n measurements from a population Population subset Selected such that Every sample of size n from the population has an equal chance of being selected. 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. 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. 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.