54 lines
1.4 KiB
Markdown
54 lines
1.4 KiB
Markdown
---
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sidebar_position: 5
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title: Sample Set Distribution
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---
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:::danger
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These functions aren't yet implemented with these specific names. This should be added soon.
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:::
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Sample set distributions are one of the three distribution formats. Internally, they are stored as a list of numbers. It's useful to distinguish point set distributions from arbitrary lists of numbers to make it clear which functions are applicable.
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Monte Carlo calculations typically result in sample set distributions.
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All regular distribution function work on sample set distributions. In addition, there are several functions that only work on sample set distributions.
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### make
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```
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SampleSet.make: (distribution) => sampleSet
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SampleSet.make: (list<number>) => sampleSet
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SampleSet.make: (() => number) => sampleSet // not yet implemented
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```
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### map
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```
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SampleSet.map: (sampleSet, (number => number)) => sampleSet
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```
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### map2
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```
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SampleSet.map2: (sampleSet, sampleSet, ((number, number) => number)) => sampleSet
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```
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### map3
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```
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SampleSet.map3: (sampleSet, sampleSet, sampleSet, ((number, number, number) => number)) => sampleSet
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```
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### toList
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```
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SampleSet.toList: (sampleSet) => list<number>
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```
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Gets the internal samples of a sampleSet distribution. This is separate from the sampleN() function, which would shuffle the samples. toList() maintains order and length.
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**Examples**
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```
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toList(toSampleSet(normal(5,2)))
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```
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