sampling
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Note: This course is being offered this summer by Stanford as an online course for credit. It can be taken individually, or as part of a master’s degree or graduate certificate earned online through the Stanford Center for Professional Development. The goals for the course are to gain a facility with using the Fourier transform, both specific techniques and general principles, and learning to recognize when, why, and how it is used. To...more
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This course serves as an introduction to the theory and practice behind many of today's communications systems. 6.450 forms the first of a two-course sequence on digital communication. The second class, 6.451, is offered in the spring. Topics covered include: digital communications at the block diagram level, data compression, Lempel-Ziv algorithm, scalar and vector quantization, sampling and aliasing, the Nyquist criterion, PAM and QAM...more
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This course uses the theory and application of atomistic computer simulations to model, understand, and predict the properties of real materials. Specific topics include: energy models from classical potentials to first-principles approaches; density functional theory and the total-energy pseudopotential method; errors and accuracy of quantitative predictions: thermodynamic ensembles, Monte Carlo sampling and molecular dynamics simulations...more
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Discrete-time fourier transforms and sampling theorem
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Introduction to the central limit theorem and the sampling distribution of the mean.
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Sample spaces, naive definition of probability, counting, sampling.
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Standard Error of the Mean (a.k.a. the standard deviation of the sampling distribution of the sample mean.
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A look ahead, final review, other statistics courses, regression example, sampling from a finite population example.
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Figuring out the probability of running out of water on a camping trip.
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More on the Central Limit Theorem and the Sampling Distribution of the Sample Mean.
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The central limit theorem and the sampling distribution of the sample mean.
