Box–Muller transform

The Box–Muller transform, by <a href="/facts/George_E._P._Box/ERu98hUq">George Edward Pelham Box</a> and <a href="/facts/Mervin_Edgar_Muller/vKD4arqt">Mervin Edgar Muller</a>, is a <a href="/facts/Random_number_sampling/xIkyQJqu">random number sampling</a> method for generating pairs of <a href="/facts/Statistical_independence/NUzQtnUL">independent</a>, standard, <a href="/facts/Normal_distribution/UapjjPyQ">normally distributed</a> (zero <a href="/facts/Expected_value/1XV0JKL8">expectation</a>, unit <a href="/facts/Variance/ULBJKXD1">variance</a>) random numbers, given a source of <a href="/facts/Uniform_distribution_(continuous)/XbnlVljT">uniformly distributed</a> random numbers. The method was first mentioned explicitly by <a href="/facts/Raymond_Paley/bu02ExE7">Raymond E. A. C. Paley</a> and <a href="/facts/Norbert_Wiener/cVMRAHCU">Norbert Wiener</a> in their 1934 treatise on Fourier transforms in the complex domain. Given the status of these latter authors and the widespread availability and use of their treatise, it is almost certain that Box and Muller were well aware of its contents.
The Box–Muller transform is commonly expressed in two forms. The basic form as given by Box and Muller takes two samples from the uniform distribution on the interval (0,1) and maps them to two standard, normally distributed samples. The polar form takes two samples from a different interval, [−1,+1], and maps them to two normally distributed samples without the use of sine or cosine functions.
The Box–Muller transform was developed as a more computationally efficient alternative to the <a href="/facts/Inverse_transform_sampling_method/fIDxBxT6">inverse transform sampling method</a>. The <a href="/facts/Ziggurat_algorithm/g0ltMb3h">ziggurat algorithm</a> gives a more efficient method for scalar processors (e.g. old CPUs), while the Box–Muller transform is superior for processors with vector units (e.g. GPUs or modern CPUs).

Box–Muller transform open-in-new

Box–Muller transform