'rejection sampling' is a correct and usable phrase in written English.
You can use it in academic writing, technical writing, and other contexts that discuss probability and statistics. For example: "Using rejection sampling, we were able to identify the most efficient choice from a range of different options."
Note that it is possible to draw easily from p t (x)∝ min{q t (x),π(x)} using the rejection sampling principle [24, 25], i.e., using the following procedure (in order to draw one sample x a ): 1. Draw (x'sim {widetilde q}_{t}(x) propto q_{t}(x)) and (u' sim mathcal {U}([0,1]!)).
The remaining model parameters with non-normalized fully conditional distributions were updated univariately using adaptive rejection sampling.
All of the fully conditional posterior distributions defining a Gibbs sampler were easy to sample from, either directly or using adaptive rejection sampling.
This allows us to compare the ABC SMC approximated posterior distributions of the models with true posterior distributions, and to demonstrate the computational efficiency of our approach when compared with model selection based on ABC rejection sampling.
This image interval provides an upper bound for the global maximum and a lower bound for the global minimum of f over each element of the partition of T. We use this information to construct an envelope as a simple function over the partition T. Using the Alias method [ 4] we efficiently propose samples from this normalized step-function envelope for von Neumann rejection sampling.
The interaction with weighting makes this strategy rejection sampling, so called because you reject some executions when you reach a conditioning statement.
This study adopts the acceptance-rejection sampling method, instead of the direct sampling method, for random variable sampling to reduce computing complexity.
This paper presents an acceptance-rejection sampling technique to simulate wind speed [37].
Weibull PDF is selected as the proposal distribution for KDF in the acceptance-rejection sampling technique.
To this end, we combine the representations presented in the latter section with the method of acceptance-rejection sampling that was introduced first in (von Neumann 1951).
It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events.
