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## Confidence and Margin of Error

❶What Is the Definition of Sampling Distribution? These types of probability sampling technique include simple random sampling , systematic random sampling , stratified random sampling and cluster sampling.

## One comment on “The importance of good sampling”

Hence, the basic methodology in importance sampling is to choose a distribution which "encourages" the important values. This use of "biased" distributions will result in a biased estimator if it is applied directly in the simulation.

However, the simulation outputs are weighted to correct for the use of the biased distribution, and this ensures that the new importance sampling estimator is unbiased. The weight is given by the likelihood ratio , that is, the Radon—Nikodym derivative of the true underlying distribution with respect to the biased simulation distribution.

The fundamental issue in implementing importance sampling simulation is the choice of the biased distribution which encourages the important regions of the input variables.

Choosing or designing a good biased distribution is the "art" of importance sampling. The rewards for a good distribution can be huge run-time savings; the penalty for a bad distribution can be longer run times than for a general Monte Carlo simulation without importance sampling. It can be shown that the following distribution minimizes the above variance: The last equality in the above equation motivates the estimator.

That is, the estimation procedure is to generate i. The variance of the importance sampling estimator is easily shown to be. For some biasing density function, which minimizes the variance, and under certain conditions reduces it to zero, it is called an optimal biasing density function.

Although there are many kinds of biasing methods, the following two methods are most widely used in the applications of importance sampling.

This results in a heavier tail of the density, leading to an increase in the event probability. Scaling is probably one of the earliest biasing methods known and has been extensively used in practice.

It is simple to implement and usually provides conservative simulation gains as compared to other methods. A modern version of importance sampling by scaling is e. In opposite to many other high yield estimation methods like worst-case distances WCD SSS does not suffer much from the dimensionality problem. Also addressing multiple MC outputs causes no degradation in efficiency. Another simple and effective biasing technique employs translation of the density function and hence random variable to place much of its probability mass in the rare event region.

Translation does not suffer from a dimensionality effect and has been successfully used in several applications relating to simulation of digital communication systems. It often provides better simulation gains than scaling. In biasing by translation, the simulation density is given by. The fundamental problem with importance sampling is that designing good biased distributions becomes more complicated as the system complexity increases.

Complex systems are the systems with long memory since complex processing of a few inputs is much easier to handle. This dimensionality or memory can cause problems in three ways:. In principle, the importance sampling ideas remain the same in these situations, but the design becomes much harder. A successful approach to combat this problem is essentially breaking down a simulation into several smaller, more sharply defined subproblems.

Then importance sampling strategies are used to target each of the simpler subproblems. Examples of techniques to break the simulation down are conditioning and error-event simulation EES and regenerative simulation. Also, if the population in question is very large, a small sample, no matter how random, does not account for all of the diversity in the main population. Systematic sampling is one way to overcome the problems of simple random sampling. Systematic sampling begins with a random sample and then continues with the sampling of every kth element, where k is a population or sample size.

A simple example is sampling a long list of people by choosing a random individual from the first 10, and then sampling every 10th person thereafter. Another method, stratified sampling, is useful when a population contains several distinct subsets. In this case, random sampling is conducted within each different subset. Why Is Sampling Important?

Quick Answer Sampling, in statistics, is a method of answering questions that deal with large numbers of individuals by selecting a smaller subset of the population for study. What Is the Definition of Sampling Distribution? Full Answer Fields of science such as biology, sociology and psychology often study questions about large populations. Learn more about Statistics.

What Is Mu in Statistics? In statistics, Mu stands for the mean of a series of numbers. The mean can also be described as the average of the numbers.

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## Main Topics

Sampling is done in research to be able to produce accurate results. It is impractical and undesirable to study the whole population and that's why sampling is done. If the sample is too small or excessively large, it may lead to incorrect finding.

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Sampling gives you the estimate of population parameters, the most common one being mean of the population. In reality it is not possible to get the inputs for study under consideration from complete population as the data collected may run into t.