survey sampling, weights can be applied to the data to adjust for the sample design, particularly in stratified sampling. Results from probability theory...

in the theory relating to sampling from finite populations, the sampling probability (also known as inclusion probability) of an element or member of...

conducting a probability sample of the household population in the United States are Area Probability Sampling, Random Digit Dial telephone sampling, and more...

In statistics, a sampling distribution or finite-sample distribution is the probability distribution of a given random-sample-based statistic. For an arbitrarily...

infinitely many trials. Probabilities can be found (in principle) by a repeatable objective process, as in repeated sampling from the same population...

description of a random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space). For instance, if X is used to denote...

Probability theory or probability calculus is the branch of mathematics concerned with probability. Although there are several different probability interpretations...

obtaining a sequence of random samples from a probability distribution from which direct sampling is difficult. New samples are added to the sequence in...

value of the random variable would be equal to that sample. Probability density is the probability per unit length, in other words, while the absolute...

the sampling probability is known, from which the sampling population is drawn from the target population, then the inverse of this probability is used...

general purpose unequal probability sampling plan". Biometrika. 69 (3): 653–656. doi:10.1093/biomet/69.3.653. Tillé, Yves (2006). Sampling Algorithms. Springer...

In probability theory, the sample space (also called sample description space, possibility space, or outcome space) of an experiment or random trial is...

phenomenon under study rather than to the method of sampling. Medical sources sometimes refer to sampling bias as ascertainment bias. Ascertainment bias has...

statistics, Gibbs sampling or a Gibbs sampler is a Markov chain Monte Carlo (MCMC) algorithm for sampling from a specified multivariate probability distribution...

cluster sampling we can use a two stage sampling in which we sample each cluster (which may be of different sizes) with equal probability, and then sample from...

model the number of successes in a sample of size n drawn with replacement from a population of size N. If the sampling is carried out without replacement...

small sample from a large population, sampling without replacement is approximately the same as sampling with replacement, since the probability of choosing...

Nonprobability sampling is a form of sampling that does not utilise random sampling techniques where the probability of getting any particular sample may be calculated...

Inverse transform sampling (also known as inversion sampling, the inverse probability integral transform, the inverse transformation method, or the Smirnov...

example, one can define a probability space which models the throwing of a die. A probability space consists of three elements: A sample space, Ω {\displaystyle...

sampling formula describes the probabilities associated with counts of how many different alleles are observed a given number of times in the sample....

Top-p sampling avoids this by setting a threshold p and then restricting the sampling to the set of most probable tokens with cumulative probability more...

In probability theory, an event is a subset of outcomes of an experiment (a subset of the sample space) to which a probability is assigned. A single outcome...

In survey methodology, probability-proportional-to-size (pps) sampling is a sampling process where each element of the population (of size N) has some...

Sampling design Sampling distribution Sampling error Sampling fraction Sampling frame Sampling probability Sampling risk Samuelson's inequality Sargan test...

Probability is a branch of mathematics and statistics concerning events and numerical descriptions of how likely they are to occur. The probability of...

p_{i}} ). If all first-order inclusion probabilities are equal, Poisson sampling becomes equivalent to Bernoulli sampling, which can therefore be considered...

sampling (also known as grab sampling, accidental sampling, or opportunity sampling) is a type of non-probability sampling that involves the sample being...

sampling is also related to umbrella sampling in computational physics. Depending on the application, the term may refer to the process of sampling from...

(MCMC) is a class of algorithms used to draw samples from a probability distribution. Given a probability distribution, one can construct a Markov chain...