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To convert a signal from continuous time to discrete time, a process called sampling is used. The value of the signal is measured at certain intervals in time. Each measurement is referred to as a sample. (The analog signal is also quantized in amplitude, but that process is ignored in this demonstration.
Sampling Methods. Probability Sampling: Each data unit in the population has a known likelihood of being included in the sample. Non-probability Sampling: Does not involve random selection; inclusion of an item is based on convenience.
Systematic Sampling: Simple Random Sampling in an ordered systematic way, e.g. every 100th name in the yellow pages ! Stratified Sampling: Population divided into different groups from which we sample randomly ! Cluster Sampling: Population is divided into (geographical) clusters - some clusters are chosen
The aim of this course is to cover sampling design and analysis methods that would be useful for research and management in many field. A well designed sampling procedure ensures that we can summarize and analyze data with a minimum of assumptions and complications.
8 Μαρ 2014 · Book PDF Available. Introduction to Modern Sampling Theory: designs, estimators and algorithms. March 2014. Publisher: LAP Publishing. ISBN: 978-3-8465-3191-4. Authors: Antonio Mura....
Using concepts from probability, this course introduces sampling from ˝nite populations and equips learners with an understanding of key fundamentals of sampling including sampling frame, design, error and inference.
Sampling Intuitions. Reconstruct the “smoothest” signal that makes sense from samples. If signal is “smooth enough”, sampling will give something we can reconstruct. If signal is not “smooth”, sampling will give something that will reconstruct to something else.
