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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" > KempSmallMeanPoissonSampler< / a > < / div >
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< h1 class = "cover" > < span > Kemp< / span > < wbr > < span > Small< / span > < wbr > < span > Mean< / span > < wbr > < span > Poisson< / span > < wbr > < span > < span > Sampler< / span > < / span > < / h1 >
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< div class = "content sourceset-depenent-content" data-active = "" data-togglable = ":kmath-stat:dokkaHtmlPartial/commonMain" > < div class = "symbol monospace" > < span class = "token keyword" > class < / span > < a href = "index.html" > KempSmallMeanPoissonSampler< / a > : < a href = "../../space.kscience.kmath.stat/-sampler/index.html" > Sampler< / a > < span class = "token operator" > < < / span > < span class = "token keyword" > < / span > < a href = "https://kotlinlang.org/api/latest/jvm/stdlib/kotlin/-int/index.html" > Int< / a > < span class = "token operator" > > < / span > < 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" > Sampler for the Poisson distribution.< / p > < ul > < li > < p class = "paragraph" > Kemp, A, W, (1981) Efficient Generation of Logarithmically Distributed Pseudo-Random Variables. Journal of the Royal Statistical Society. Vol. 30, No. 3, pp. 249-253. This sampler is suitable for mean < 40. For large means, LargeMeanPoissonSampler should be used instead.< / p > < / li > < / ul > < p class = "paragraph" > Note: The algorithm uses a recurrence relation to compute the Poisson probability and a rolling summation for the cumulative probability. When the mean is large the initial probability (Math.exp(-mean)) is zero and an exception is raised by the constructor.< / p > < p class = "paragraph" > Sampling uses 1 call to UniformRandomProvider.nextDouble(). This method provides an alternative to the SmallMeanPoissonSampler for slow generators of double.< / 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/KempSmallMeanPoissonSampler.html.< / p > < / div > < / div >
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< div > < a href = "sample.html" > < span > < span > sample< / span > < / span > < / a > < / div >
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< div class = "content sourceset-depenent-content" data-active = "" data-togglable = ":kmath-stat:dokkaHtmlPartial/commonMain" > < div class = "symbol monospace" > < span class = "token keyword" > open < / span > < span class = "token keyword" > override < / span > < span class = "token keyword" > fun < / span > < a href = "sample.html" > < span class = "token function" > sample< / span > < / a > < span class = "token punctuation" > (< / span > generator< span class = "token operator" > : < / span > < a href = "../../space.kscience.kmath.stat/-random-generator/index.html" > RandomGenerator< / a > < span class = "token punctuation" > )< / span > < span class = "token operator" > : < / span > < a href = "../../../kmath-coroutines/space.kscience.kmath.chains/-blocking-int-chain/index.html" > BlockingIntChain< / 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 class = "brief " > < p class = "paragraph" > Generates a chain of samples.< / p > < / div > < / div > < / div >
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< h2 class = "" > Sources< / h2 >
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