CacheDiff: Fast Random Sampling
نویسنده
چکیده
We present a sampling method called, CacheDiff, that has both time and space complexity of O(k) to randomly select k items from a pool of N items, in which N is known.
منابع مشابه
Projecting Markov Random Field Parameters for Fast Mixing
Markov chain Monte Carlo (MCMC) algorithms are simple and extremely powerful techniques to sample from almost arbitrary distributions. The flaw in practice is that it can take a large and/or unknown amount of time to converge to the stationary distribution. This paper gives sufficient conditions to guarantee that univariate Gibbs sampling on Markov Random Fields (MRFs) will be fast mixing, in a...
متن کاملContinuous Fast Fourier Sampling
Fourier sampling algorithms exploit the spectral sparsity of a signal to reconstruct it quickly from a small number of samples. In these algorithms, the sampling rate is subNyquist and the time to reconstruct the dominate frequencies depends on the type of algorithm—some scale with the number of tones found and others with the length of the signal. The Ann Arbor Fast Fourier Transform (AAFFT) s...
متن کاملA fast algorithm for computing the sampling distribution of a statistic from discrete populations
In this work we propose a fast algorithm for computing the exact small sampling distribution of a given statistic, when the population random variable is discrete. The algorithm relies on a recursion on block matrices that describes all possible random samples that can be generated. In this way, the power of modern programming which deÞnes objects in term of matrices is fully exploited for effe...
متن کاملFast Random Sampling of Triangular Meshes
We present a simple and fast algorithm for generating randomly distributed points on a triangle mesh with probability density specified by a two-dimensional texture. Efficiency is achieved by resampling the density texture on an adaptively subdivided version of the input mesh. This allows us to generate the samples up to 40× faster than the rejection sampling algorithm, the fastest existing alt...
متن کاملDynamic random Weyl sampling for drastic reduction of randomness in Monte Carlo integration
To reduce randomness drastically in Monte Carlo (MC) integration, we propose a pairwise independent sampling, the dynamic random Weyl sampling (DRWS). DRWS is applicable even if the length of random bits to generate a sample may vary. The algorithm of DRWS is so simple that it works very fast, even though the pseudo-random generator, the source of randomness, might be slow. In particular, we ca...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- CoRR
دوره abs/1512.00501 شماره
صفحات -
تاریخ انتشار 2015