PPT-Data Sampling &

Nyquist Theorem Richa Sharma Dept of Physics And Astrophysics University of Delhi Signal Any physical quantity that varies with time space or any other independent variable or variables. Impact Evaluations. Marie-H. é. lène. . Cloutier. 1. Introduction. Ideally, want to compare what happens to the . same. schools with and without the program. But . impossible. → use . statistics. 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 Zhizhuo. Zhang . Outline. Review of Mixture Model and EM algorithm. Importance Sampling. Re-sampling EM. Extending EM. Integrate Other Features. Result. Review Motif Finding: Mixture modeling. Given a dataset . Sampling And AliasingSampling And AliasingWhat is sampling?What is sampling?–– The Process of conversion of analog signal to the The Process of conversion of analog signal to the digital sig , Identification and Testing. (S.I.T.) . Introduction. Define basic principles for applying sampling, identification and testing requirements. 1) . Systems and procedures. ensuring that samples are representative of the batch when sampled. How do Sociologists choose the participants for their research?. Starter. Think. - Work independently for 2 minutes to write in as many key concepts into the worksheet as you can. . Pair. - Now, work in a pair with the person sitting next to you for 2 minutes and help each other with any key concepts you couldn't do on your own.. Robert Christensen. , Feifei Li. University of Utah. Lu Wang, Ke Yi. Hong Kong University. Of Science and Technology. Motivation. Geo Spatial Data is being collected on a massive scale. Approximate aggregations is fast and often effective for this data. Lemonade from Lemons. Bugs manifest themselves every where in deployed systems.. Each manifestation gives us the chance of inspection and hence the resolutions.. Deployment gives more test cases than the test suite.. A link between Continuous-time/Discrete-time Systems. x. (. t. ). y. (. t. ). h. (. t. ). x. [. n. ]. y. [. n. ]. h. [. n. ]. Sampling. x. [. n. ]=. x. (. nT. ), . T. : sampling period. x. [. n. ]. x. YR04 Efforts. MFRPA Alliance and Lab Accreditation Meeting. Louisville, KY February 2, 2016. Overview of . S. ampling Working Group Efforts. Publication of the “Guidance on Obtaining Defensible Samples” (. General Sampling Issues. Thinking of the steps in sampling (from theoretical population to respondents)—what are some biases that can come in at each point?. What is the proximity similarity model? What are issues with that model?. 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.