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< div class = "breadcrumbs" > < a href = "../../index.html" > kmath-stat< / a > /< a href = "../index.html" > space.kscience.kmath.samplers< / a > /< a href = "index.html" > ZigguratNormalizedGaussianSampler< / a > < / div >
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< h1 class = "cover" > < span > Ziggurat< / span > < wbr > < span > Normalized< / span > < wbr > < span > Gaussian< / span > < wbr > < span > Sampler< / span > < / h1 >
< div class = "platform-hinted " data-platform-hinted = "data-platform-hinted" > < div class = "content sourceset-depenent-content" data-active = "" data-togglable = ":kmath-stat:dokkaHtmlPartial/commonMain" > < div class = "symbol monospace" > object < a href = "index.html" > ZigguratNormalizedGaussianSampler< / a > : < a href = "../-normalized-gaussian-sampler/index.html" > NormalizedGaussianSampler< / a > < span class = "top-right-position" > < span class = "copy-icon" > < / span > < div class = "copy-popup-wrapper popup-to-left" > < span class = "copy-popup-icon" > < / span > < span > Content copied to clipboard< / span > < / div > < / span > < / div > < p class = "paragraph" > < a href = "https://en.wikipedia.org/wiki/Ziggurat_algorithm" > Marsaglia and Tsang "Ziggurat"< / a > method for sampling from a Gaussian distribution with mean 0 and standard deviation 1. The algorithm is explained in this paper and this implementation has been adapted from the C code provided therein.< / p > < p class = "paragraph" > Based on Commons RNG implementation. See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.html.< / p > < / div > < / div >
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< div class = "platform-hinted " data-platform-hinted = "data-platform-hinted" > < div class = "content sourceset-depenent-content" data-active = "" data-togglable = ":kmath-stat:dokkaHtmlPartial/commonMain" > < div class = "symbol monospace" > open override fun < a href = "sample.html" > sample< / a > (generator: < a href = "../../space.kscience.kmath.stat/-random-generator/index.html" > RandomGenerator< / a > ): < a href = "../../../kmath-coroutines/space.kscience.kmath.chains/-blocking-double-chain/index.html" > BlockingDoubleChain< / a > < span class = "top-right-position" > < span class = "copy-icon" > < / span > < div class = "copy-popup-wrapper popup-to-left" > < span class = "copy-popup-icon" > < / span > < span > Content copied to clipboard< / span > < / div > < / span > < / div > < / div > < / div >
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< a data-name = "695492037%2FFunctions%2F-443365019" anchor-label = "sample" id = "695492037%2FFunctions%2F-443365019" data-filterable-set = ":kmath-stat:dokkaHtmlPartial/commonMain" > < / a > < div class = "brief " > < a data-name = "695492037%2FFunctions%2F-443365019" anchor-label = "sample" id = "695492037%2FFunctions%2F-443365019" data-filterable-set = ":kmath-stat:dokkaHtmlPartial/commonMain" > < / a > Generates a chain of samples.< / div > < / div >
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< h2 class = "" > Sources< / h2 >
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